Lab 3 - Machine Learning Instructor.ipynb

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    "metadata": {
        "language_info": {
            "pygments_lexer": "ipython2", 
            "name": "python", 
            "file_extension": ".py", 
            "mimetype": "text/x-python", 
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        "kernelspec": {
            "language": "python", 
            "name": "python2-spark20", 
            "display_name": "Python 2 with Spark 2.0"
        }
    }, 
    "cells": [
        {
            "source": "# Machine learning - Instructor notebook\n   \n   - Dataset, RDD APIs\n   - A dataframe is nothing but a table with columns, rows and headers\n   - In this notebook, we will work through a dataset to demostrate spark's SQL-like abilities. We will also look at its machine learning capabilities in context of this dataset.\n    \n### Dataset\n- The chosen dataset is that of Breast Cancer. This set is collected from digitized image of a fine needle aspirate (FNA) of a breast mass. For more information about this dataset, please visit https://www.kaggle.com/uciml/breast-cancer-wisconsin-data\n- Each row contains information about a single breast mass along with a diagnosis of malignant/benign for this mass\n    \n### Problem statement\n- We need to learn patterns describing malignant and benign masses and need to be able to place any future samples in either of these buckets.  \n- Thus, the problem is a classification one where given a set of probable results, we need to pick one result with confidance. \n    \n### Solution\n   We will go through the following steps:\n   - Read data (Spark SQL)\n   - Feature Engineering (Accumulators, Broadcasters, ml/mllib APIs)\n   - Data Visualizations (PixieDust, Seaborn)\n   - Modeling (Spark ML)\n   - Evaluation and prediction (Spark ML)\n   - Deployment (Watson ML repository)", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 1, 
            "source": "from IPython.display import display\nfrom IPython.core.display import HTML \n\n\n\nfrom IPython.display import HTML\n\nHTML('''<script>\ncode_show=true; \nfunction code_toggle() {\n if (code_show){\n $('div.input').hide();\n } else {\n $('div.input').show();\n }\n code_show = !code_show\n} \n$( document ).ready(code_toggle);\n</script>\n<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "execution_count": 1, 
                    "data": {
                        "text/html": "<script>\ncode_show=true; \nfunction code_toggle() {\n if (code_show){\n $('div.input').hide();\n } else {\n $('div.input').show();\n }\n code_show = !code_show\n} \n$( document ).ready(code_toggle);\n</script>\n<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>", 
                        "text/plain": "<IPython.core.display.HTML object>"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ]
        }, 
        {
            "execution_count": 2, 
            "source": "!pip install seaborn", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Requirement already satisfied: seaborn in /gpfs/global_fs01/sym_shared/YPProdSpark/user/s280-a46e86a84e2274-95b1885704d4/.local/lib/python2.7/site-packages\r\n"
                }
            ]
        }, 
        {
            "source": "#### Machine Learning process ###\n\n<center><img width=\"600px\" height=\"600px\" src=\"https://raw.githubusercontent.com/martinkearn/Content/master/Blogs/Images/MLProcess.PNG\"></center>", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "### Get dataset\n!wget https://raw.githubusercontent.com/joshishwetha/dsx-spark/master/data.csv", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 4, 
            "source": "!wget https://raw.githubusercontent.com/joshishwetha/dsx-spark/master/data.csv", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "--2017-08-03 11:42:49--  https://raw.githubusercontent.com/joshishwetha/dsx-spark/master/data.csv\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: 125139 (122K) [text/plain]\nSaving to: \u2018data.csv.11\u2019\n\n100%[======================================>] 125,139     --.-K/s   in 0.005s  \n\n2017-08-03 11:42:50 (24.7 MB/s) - \u2018data.csv.11\u2019 saved [125139/125139]\n\n"
                }
            ]
        }, 
        {
            "source": "### Read dataset\n\n#### copy and paste the following code\n    data = spark.read.csv('data.csv',inferSchema='true',header='true')\n    data = data.drop('_c32')", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 1, 
            "source": "data = spark.read.csv('data.csv',inferSchema='true',header='true')\ndata = data.drop('_c32')", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "execution_count": 2, 
            "source": "pyspark.sql", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "execution_count": 2, 
                    "data": {
                        "text/plain": "<module 'pyspark.sql' from '/usr/local/src/spark20master/spark/python/pyspark/sql/__init__.py'>"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ]
        }, 
        {
            "source": "## Data Exploration ###", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "<center><img width=\"450px\" height=\"450px\" margin=\"auto\" src=\"https://www.colourbox.com/preview/18975116-explorer-kids.jpg\"></center>", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "#### Basic operations on dataframe \n- To view contents: df.show(), df.take(n)\n- To transform columns: df.withColumn (\"column_name\",\"transformation\")\n- To rename columns: df.withColumnRenamed(\"old_name\",\"new_name\")", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "#### Print first 3 rows of the input dataset ###\n\n    data.toPandas()[:3]", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 3, 
            "source": "data.toPandas()[:3]", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "execution_count": 3, 
                    "data": {
                        "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>diagnosis</th>\n      <th>radius_mean</th>\n      <th>texture_mean</th>\n      <th>perimeter_mean</th>\n      <th>area_mean</th>\n      <th>smoothness_mean</th>\n      <th>compactness_mean</th>\n      <th>concavity_mean</th>\n      <th>concave points_mean</th>\n      <th>...</th>\n      <th>radius_worst</th>\n      <th>texture_worst</th>\n      <th>perimeter_worst</th>\n      <th>area_worst</th>\n      <th>smoothness_worst</th>\n      <th>compactness_worst</th>\n      <th>concavity_worst</th>\n      <th>concave points_worst</th>\n      <th>symmetry_worst</th>\n      <th>fractal_dimension_worst</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>842302</td>\n      <td>M</td>\n      <td>17.99</td>\n      <td>10.38</td>\n      <td>122.8</td>\n      <td>1001.0</td>\n      <td>0.11840</td>\n      <td>0.27760</td>\n      <td>0.3001</td>\n      <td>0.14710</td>\n      <td>...</td>\n      <td>25.38</td>\n      <td>17.33</td>\n      <td>184.6</td>\n      <td>2019.0</td>\n      <td>0.1622</td>\n      <td>0.6656</td>\n      <td>0.7119</td>\n      <td>0.2654</td>\n      <td>0.4601</td>\n      <td>0.11890</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>842517</td>\n      <td>M</td>\n      <td>20.57</td>\n      <td>17.77</td>\n      <td>132.9</td>\n      <td>1326.0</td>\n      <td>0.08474</td>\n      <td>0.07864</td>\n      <td>0.0869</td>\n      <td>0.07017</td>\n      <td>...</td>\n      <td>24.99</td>\n      <td>23.41</td>\n      <td>158.8</td>\n      <td>1956.0</td>\n      <td>0.1238</td>\n      <td>0.1866</td>\n      <td>0.2416</td>\n      <td>0.1860</td>\n      <td>0.2750</td>\n      <td>0.08902</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>84300903</td>\n      <td>M</td>\n      <td>19.69</td>\n      <td>21.25</td>\n      <td>130.0</td>\n      <td>1203.0</td>\n      <td>0.10960</td>\n      <td>0.15990</td>\n      <td>0.1974</td>\n      <td>0.12790</td>\n      <td>...</td>\n      <td>23.57</td>\n      <td>25.53</td>\n      <td>152.5</td>\n      <td>1709.0</td>\n      <td>0.1444</td>\n      <td>0.4245</td>\n      <td>0.4504</td>\n      <td>0.2430</td>\n      <td>0.3613</td>\n      <td>0.08758</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows \u00d7 32 columns</p>\n</div>", 
                        "text/plain": "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n0    842302         M        17.99         10.38           122.8     1001.0   \n1    842517         M        20.57         17.77           132.9     1326.0   \n2  84300903         M        19.69         21.25           130.0     1203.0   \n\n   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n0          0.11840           0.27760          0.3001              0.14710   \n1          0.08474           0.07864          0.0869              0.07017   \n2          0.10960           0.15990          0.1974              0.12790   \n\n            ...             radius_worst  texture_worst  perimeter_worst  \\\n0           ...                    25.38          17.33            184.6   \n1           ...                    24.99          23.41            158.8   \n2           ...                    23.57          25.53            152.5   \n\n   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n0      2019.0            0.1622             0.6656           0.7119   \n1      1956.0            0.1238             0.1866           0.2416   \n2      1709.0            0.1444             0.4245           0.4504   \n\n   concave points_worst  symmetry_worst  fractal_dimension_worst  \n0                0.2654          0.4601                  0.11890  \n1                0.1860          0.2750                  0.08902  \n2                0.2430          0.3613                  0.08758  \n\n[3 rows x 32 columns]"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ]
        }, 
        {
            "source": "### Check datatypes\n    data.dtypes", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 4, 
            "source": "data.dtypes", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "execution_count": 4, 
                    "data": {
                        "text/plain": "[('id', 'int'),\n ('diagnosis', 'string'),\n ('radius_mean', 'double'),\n ('texture_mean', 'double'),\n ('perimeter_mean', 'double'),\n ('area_mean', 'double'),\n ('smoothness_mean', 'double'),\n ('compactness_mean', 'double'),\n ('concavity_mean', 'double'),\n ('concave points_mean', 'double'),\n ('symmetry_mean', 'double'),\n ('fractal_dimension_mean', 'double'),\n ('radius_se', 'double'),\n ('texture_se', 'double'),\n ('perimeter_se', 'double'),\n ('area_se', 'double'),\n ('smoothness_se', 'double'),\n ('compactness_se', 'double'),\n ('concavity_se', 'double'),\n ('concave points_se', 'double'),\n ('symmetry_se', 'double'),\n ('fractal_dimension_se', 'double'),\n ('radius_worst', 'double'),\n ('texture_worst', 'double'),\n ('perimeter_worst', 'double'),\n ('area_worst', 'double'),\n ('smoothness_worst', 'double'),\n ('compactness_worst', 'double'),\n ('concavity_worst', 'double'),\n ('concave points_worst', 'double'),\n ('symmetry_worst', 'double'),\n ('fractal_dimension_worst', 'double')]"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ]
        }, 
        {
            "source": "#### Make sure the data types are correct ####\n\n    from pyspark.sql.types import FloatType, StringType, IntegerType, DoubleType, ArrayType\n\n    for col in data.columns:\n        if col not in ['id','diagnosis']:\n            data = data.withColumn(col,data[col].cast(FloatType()))", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 5, 
            "source": "from pyspark.sql.types import FloatType, StringType, IntegerType, DoubleType, ArrayType\n\nfor col in data.columns:\n    if col not in ['id','diagnosis']:\n        data = data.withColumn(col,data[col].cast(FloatType()))", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "source": "#### Replace null values with 0\n    data = data.na.fill(0)", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 6, 
            "source": "data = data.na.fill(0)", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "source": "### SQL\n\n#### Register dataframe as a temp table to query from (write sql on dataframes)\n    data.registerTempTable('cancer_data')", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 7, 
            "source": "data.registerTempTable('cancer_data')", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "source": "#### Querying data\n- NOTE that the returned object is another Dataframe\n- One nice feature of the notebooks and python is that we can show it in a table via Pandas\n- Remember to perform an action to get your results (sql queries are also transformations :))", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "    query = \"\"\"\n    select\n        diagnosis ,\n        count(1) as diagnosis_count\n    from cancer_data\n    group by diagnosis \n    \"\"\"\n    spark.sql(query).toPandas()\n", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 8, 
            "source": "query = \"\"\"\n    select\n        diagnosis ,\n        count(1) as diagnosis_count\n    from cancer_data\n    group by diagnosis \n    \"\"\"\nspark.sql(query).toPandas()", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "execution_count": 8, 
                    "data": {
                        "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>diagnosis</th>\n      <th>diagnosis_count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>B</td>\n      <td>357</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M</td>\n      <td>212</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  diagnosis  diagnosis_count\n0         B              357\n1         M              212"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ]
        }, 
        {
            "source": "## Feature Engineering\n   - Dataset = Features + target\n   - Transforming features (categorical to numeric, continous to bins, scaling, normalization etc...)\n   - Selecting a subset of columns for wide datasets\n   - Exploding columns to make additional (synthetic features) for small datasets\n   \n<img width=\"450px\" height=\"450px\" margin=\"auto\" src=\"https://media.licdn.com/mpr/mpr/AAEAAQAAAAAAAAiuAAAAJGRkNGQzNjU5LTQzOTAtNDRlZC05NTZkLTcxZWM5YmYwZmRkNA.png\"></img>", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "### Feature set is numeric\n\n##### Step 1: Make sure your target is numeric\n- \"target\" is categorical (malignant/benign)\n- Our encoding: 1 = malignant(M), 0 = Benign(B) using sql like \"when\" statement", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "### Review your data\n#### Sample your data to view what it looks like before we make changes to the target column\n\n    data.select('diagnosis','radius_se').show(3)", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 9, 
            "source": "data.select('diagnosis','radius_se').show(3)", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "+---------+---------+\n|diagnosis|radius_se|\n+---------+---------+\n|        M|    1.095|\n|        M|   0.5435|\n|        M|   0.7456|\n+---------+---------+\nonly showing top 3 rows\n\n"
                }
            ]
        }, 
        {
            "source": "### Make label numeric\n\n##### copy and paste to run code\n    import  pyspark.sql.functions as F\n\n    data = data.withColumn('diagnosis',F.when(data.diagnosis=='M',1).otherwise(0))\n    data.toPandas().head(3)", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 10, 
            "source": "import  pyspark.sql.functions as F\n\ndata = data.withColumn('diagnosis',F.when(data.diagnosis=='M',1).otherwise(0))\ndata.toPandas().head(3)", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "execution_count": 10, 
                    "data": {
                        "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>diagnosis</th>\n      <th>radius_mean</th>\n      <th>texture_mean</th>\n      <th>perimeter_mean</th>\n      <th>area_mean</th>\n      <th>smoothness_mean</th>\n      <th>compactness_mean</th>\n      <th>concavity_mean</th>\n      <th>concave points_mean</th>\n      <th>...</th>\n      <th>radius_worst</th>\n      <th>texture_worst</th>\n      <th>perimeter_worst</th>\n      <th>area_worst</th>\n      <th>smoothness_worst</th>\n      <th>compactness_worst</th>\n      <th>concavity_worst</th>\n      <th>concave points_worst</th>\n      <th>symmetry_worst</th>\n      <th>fractal_dimension_worst</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>842302</td>\n      <td>1</td>\n      <td>17.990000</td>\n      <td>10.38</td>\n      <td>122.800003</td>\n      <td>1001.0</td>\n      <td>0.11840</td>\n      <td>0.27760</td>\n      <td>0.3001</td>\n      <td>0.14710</td>\n      <td>...</td>\n      <td>25.379999</td>\n      <td>17.330000</td>\n      <td>184.600006</td>\n      <td>2019.0</td>\n      <td>0.1622</td>\n      <td>0.6656</td>\n      <td>0.7119</td>\n      <td>0.2654</td>\n      <td>0.4601</td>\n      <td>0.11890</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>842517</td>\n      <td>1</td>\n      <td>20.570000</td>\n      <td>17.77</td>\n      <td>132.899994</td>\n      <td>1326.0</td>\n      <td>0.08474</td>\n      <td>0.07864</td>\n      <td>0.0869</td>\n      <td>0.07017</td>\n      <td>...</td>\n      <td>24.990000</td>\n      <td>23.410000</td>\n      <td>158.800003</td>\n      <td>1956.0</td>\n      <td>0.1238</td>\n      <td>0.1866</td>\n      <td>0.2416</td>\n      <td>0.1860</td>\n      <td>0.2750</td>\n      <td>0.08902</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>84300903</td>\n      <td>1</td>\n      <td>19.690001</td>\n      <td>21.25</td>\n      <td>130.000000</td>\n      <td>1203.0</td>\n      <td>0.10960</td>\n      <td>0.15990</td>\n      <td>0.1974</td>\n      <td>0.12790</td>\n      <td>...</td>\n      <td>23.570000</td>\n      <td>25.530001</td>\n      <td>152.500000</td>\n      <td>1709.0</td>\n      <td>0.1444</td>\n      <td>0.4245</td>\n      <td>0.4504</td>\n      <td>0.2430</td>\n      <td>0.3613</td>\n      <td>0.08758</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows \u00d7 32 columns</p>\n</div>", 
                        "text/plain": "         id  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n0    842302          1    17.990000         10.38      122.800003     1001.0   \n1    842517          1    20.570000         17.77      132.899994     1326.0   \n2  84300903          1    19.690001         21.25      130.000000     1203.0   \n\n   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n0          0.11840           0.27760          0.3001              0.14710   \n1          0.08474           0.07864          0.0869              0.07017   \n2          0.10960           0.15990          0.1974              0.12790   \n\n            ...             radius_worst  texture_worst  perimeter_worst  \\\n0           ...                25.379999      17.330000       184.600006   \n1           ...                24.990000      23.410000       158.800003   \n2           ...                23.570000      25.530001       152.500000   \n\n   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n0      2019.0            0.1622             0.6656           0.7119   \n1      1956.0            0.1238             0.1866           0.2416   \n2      1709.0            0.1444             0.4245           0.4504   \n\n   concave points_worst  symmetry_worst  fractal_dimension_worst  \n0                0.2654          0.4601                  0.11890  \n1                0.1860          0.2750                  0.08902  \n2                0.2430          0.3613                  0.08758  \n\n[3 rows x 32 columns]"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ]
        }, 
        {
            "source": "#### Rename column from \"diagnosis\" to \"label\"\n\n    data = data.withColumnRenamed('diagnosis','label')", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 11, 
            "source": "data = data.withColumnRenamed('diagnosis','label')", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "source": "***********************************************************************************\n\n###   Detour  \n- Feature engineering is a major task and often involves complicated scripts\n\nWhat happens when\n   - Have custom script based on other datasets/static variables?\n   - I want to port over a current script to spark ?", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "### Accumulators, Broadcasters\n- One of the most basic things in scripts are variables (data and metadata). \n- Data vars = DataFrames/RDDs in spark\n- Metadata vars = Accumulators/broadcasters \n   \n- Metadata vars are not straightforward in the distributed world\n\n\n##### Accumulators\n   - Global variables which can be written into\n   \n##### Broadcasters\n   - Global variables to be read from \n************************************************************************************************", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "#### Lets run a simple accumulator\n- Problem: Count the number of benign and malignant cases", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 12, 
            "source": "def count_labels(row):\n    global benign, malignant\n    if row.label==1:\n        malignant.add(1)\n    else:\n        benign.add(1)", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "source": "#### copy and run this code\n    benign = sc.accumulator(0)\n    malignant = sc.accumulator(0)\n\n    data.rdd.foreach(count_labels)\n\n    benign.value, malignant.value", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 13, 
            "source": "benign = sc.accumulator(0)\nmalignant = sc.accumulator(0)\n\ndata.rdd.foreach(count_labels)\n\nbenign.value, malignant.value", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "execution_count": 13, 
                    "data": {
                        "text/plain": "(357, 212)"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ]
        }, 
        {
            "source": "#### Broadcast the data variable so that each executor has its own version of it\n   - This way, it does need not be shipped to the executor with every call\n   - Saves network bandwidth", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "### Collect statistics for your dataset ###\n- Collect statistics for mean/std\n- store it in a variable called \"df\"\n\n\n        df = data.describe()\n        df.toPandas()", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 14, 
            "source": "df = data.describe()\ndf.toPandas()", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "execution_count": 14, 
                    "data": {
                        "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>summary</th>\n      <th>id</th>\n      <th>label</th>\n      <th>radius_mean</th>\n      <th>texture_mean</th>\n      <th>perimeter_mean</th>\n      <th>area_mean</th>\n      <th>smoothness_mean</th>\n      <th>compactness_mean</th>\n      <th>concavity_mean</th>\n      <th>...</th>\n      <th>radius_worst</th>\n      <th>texture_worst</th>\n      <th>perimeter_worst</th>\n      <th>area_worst</th>\n      <th>smoothness_worst</th>\n      <th>compactness_worst</th>\n      <th>concavity_worst</th>\n      <th>concave points_worst</th>\n      <th>symmetry_worst</th>\n      <th>fractal_dimension_worst</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>count</td>\n      <td>569</td>\n      <td>569</td>\n      <td>569</td>\n      <td>569</td>\n      <td>569</td>\n      <td>569</td>\n      <td>569</td>\n      <td>569</td>\n      <td>569</td>\n      <td>...</td>\n      <td>569</td>\n      <td>569</td>\n      <td>569</td>\n      <td>569</td>\n      <td>569</td>\n      <td>569</td>\n      <td>569</td>\n      <td>569</td>\n      <td>569</td>\n      <td>569</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>mean</td>\n      <td>3.0371831432337433E7</td>\n      <td>0.37258347978910367</td>\n      <td>14.127291743072348</td>\n      <td>19.289648528677297</td>\n      <td>91.96903329993384</td>\n      <td>654.889103814043</td>\n      <td>0.09636028129312821</td>\n      <td>0.10434098429781481</td>\n      <td>0.08879931578830029</td>\n      <td>...</td>\n      <td>16.269189776770887</td>\n      <td>25.67722316534113</td>\n      <td>107.26121279644421</td>\n      <td>880.5831290514901</td>\n      <td>0.13236859435565862</td>\n      <td>0.25426504394016597</td>\n      <td>0.2721884833807977</td>\n      <td>0.11460622294146325</td>\n      <td>0.2900755708948799</td>\n      <td>0.08394581713895387</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>stddev</td>\n      <td>1.2502058561222367E8</td>\n      <td>0.4839179564031687</td>\n      <td>3.524048812967195</td>\n      <td>4.301035792275387</td>\n      <td>24.298980946187076</td>\n      <td>351.91412886139733</td>\n      <td>0.014064128011679866</td>\n      <td>0.05281275807458227</td>\n      <td>0.07971980885275734</td>\n      <td>...</td>\n      <td>4.8332415912724365</td>\n      <td>6.146257611231105</td>\n      <td>33.602542264508905</td>\n      <td>569.3569923849643</td>\n      <td>0.022832429559187094</td>\n      <td>0.15733648854662943</td>\n      <td>0.20862428007810724</td>\n      <td>0.06573234105890066</td>\n      <td>0.06186746818484165</td>\n      <td>0.01806126727675421</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>min</td>\n      <td>8670</td>\n      <td>0</td>\n      <td>6.981</td>\n      <td>9.71</td>\n      <td>43.79</td>\n      <td>143.5</td>\n      <td>0.05263</td>\n      <td>0.01938</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>7.93</td>\n      <td>12.02</td>\n      <td>50.41</td>\n      <td>185.2</td>\n      <td>0.07117</td>\n      <td>0.02729</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.1565</td>\n      <td>0.05504</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>max</td>\n      <td>911320502</td>\n      <td>1</td>\n      <td>28.11</td>\n      <td>39.28</td>\n      <td>188.5</td>\n      <td>2501.0</td>\n      <td>0.1634</td>\n      <td>0.3454</td>\n      <td>0.4268</td>\n      <td>...</td>\n      <td>36.04</td>\n      <td>49.54</td>\n      <td>251.2</td>\n      <td>4254.0</td>\n      <td>0.2226</td>\n      <td>1.058</td>\n      <td>1.252</td>\n      <td>0.291</td>\n      <td>0.6638</td>\n      <td>0.2075</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 33 columns</p>\n</div>", 
                        "text/plain": "  summary                    id                label         radius_mean  \\\n0   count                   569                  569                 569   \n1    mean  3.0371831432337433E7  0.37258347978910367  14.127291743072348   \n2  stddev  1.2502058561222367E8   0.4839179564031687   3.524048812967195   \n3     min                  8670                    0               6.981   \n4     max             911320502                    1               28.11   \n\n         texture_mean      perimeter_mean           area_mean  \\\n0                 569                 569                 569   \n1  19.289648528677297   91.96903329993384    654.889103814043   \n2   4.301035792275387  24.298980946187076  351.91412886139733   \n3                9.71               43.79               143.5   \n4               39.28               188.5              2501.0   \n\n        smoothness_mean     compactness_mean       concavity_mean  \\\n0                   569                  569                  569   \n1   0.09636028129312821  0.10434098429781481  0.08879931578830029   \n2  0.014064128011679866  0.05281275807458227  0.07971980885275734   \n3               0.05263              0.01938                  0.0   \n4                0.1634               0.3454               0.4268   \n\n            ...                  radius_worst      texture_worst  \\\n0           ...                           569                569   \n1           ...            16.269189776770887  25.67722316534113   \n2           ...            4.8332415912724365  6.146257611231105   \n3           ...                          7.93              12.02   \n4           ...                         36.04              49.54   \n\n      perimeter_worst         area_worst      smoothness_worst  \\\n0                 569                569                   569   \n1  107.26121279644421  880.5831290514901   0.13236859435565862   \n2  33.602542264508905  569.3569923849643  0.022832429559187094   \n3               50.41              185.2               0.07117   \n4               251.2             4254.0                0.2226   \n\n     compactness_worst      concavity_worst concave points_worst  \\\n0                  569                  569                  569   \n1  0.25426504394016597   0.2721884833807977  0.11460622294146325   \n2  0.15733648854662943  0.20862428007810724  0.06573234105890066   \n3              0.02729                  0.0                  0.0   \n4                1.058                1.252                0.291   \n\n        symmetry_worst fractal_dimension_worst  \n0                  569                     569  \n1   0.2900755708948799     0.08394581713895387  \n2  0.06186746818484165     0.01806126727675421  \n3               0.1565                 0.05504  \n4               0.6638                  0.2075  \n\n[5 rows x 33 columns]"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ]
        }, 
        {
            "source": "#### Calculate correlations of each columns w.r.t the target\n- Change to do this in spark\n\n\n\n#### Select a subset of columns\n- Remove columns conveying very little information\n   - Zero variance columns\n   - Low correlation columns", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 15, 
            "source": "df = data.toPandas()\ncol_corr = sorted(df.corr()['label'].to_dict().items(),key=lambda x:x[1],reverse=True)\nfor col in col_corr[-5:]:\n    if col[0]!='id':\n        data = data.drop(col[0])", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "source": "- Remove columns with low correlation, such columns contribute to very little information\n- Caution: Be wary of multicolinearity (\"leaky\" columns)", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "## Lets do some graphs!\n\n###  Here, we look at the columns and their correlations with the target variable.\n- We use PixieDust (charting library) for quick charts\n- Also takes RDDs as inputs, most of the other charting librabries take pandas dataframes as inputs\n\n\n##### copy and run code from here\n    import pixiedust\n    import pandas as pd\n    \n    df = pd.DataFrame(col_corr)\n    df = df.dropna()\n    df.columns = ['name','correlation']\n    viz_spark_df = sqlContext.createDataFrame(df)\n\n   \n    display(viz_spark_df)", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 16, 
            "source": "%matplotlib inline \n\nimport pixiedust\nimport pandas as pd\n\ndf = pd.DataFrame(col_corr)\ndf = df.dropna()\ndf.columns = ['name','correlation']\nviz_spark_df = sqlContext.createDataFrame(df)\n\ndisplay(viz_spark_df)", 
            "metadata": {
                "pixiedust": {
                    "displayParams": {
                        "aggregation": "SUM", 
                        "valueFields": "correlation", 
                        "handlerId": "barChart", 
                        "rowCount": "100", 
                        "keyFields": "name", 
                        "rendererId": "bokeh", 
                        "legend": "false"
                    }
                }
            }, 
            "cell_type": "code", 
            "outputs": [
                {
                    "data": {
                        "text/html": "<style type=\"text/css\">.pd_warning{display:none;}</style><div class=\"pd_warning\"><em>Hey, there's something awesome here! To see it, open this notebook outside GitHub, in a viewer like Jupyter</em></div>\n        <div class=\"pd_save is-viewer-good\" style=\"padding-right:10px;text-align: center;line-height:initial !important;font-size: xx-large;font-weight: 500;color: coral;\">\n            \n        </div>\n    <div id=\"chartFigure4329075d\" class=\"pd_save is-viewer-good\" style=\"overflow-x:auto\">\n            \n                    \n\n    <div class=\"bk-root\">\n        <div class=\"bk-grid-column bk-layout-fixed\" id=\"modelid_2a95aad6-081f-47bf-9dce-93684fa83071\" style=\"width: 714px; height: 564px;\"><div class=\"bk-toolbar-box bk-layout-scale_width\" id=\"modelid_e0d96339-5dc4-4cfe-8685-5160c12800bf\" style=\"width: 714px; height: 30px;\"><div class=\"bk-toolbar-wrapper bk-layout-scale_width\" id=\"modelid_ToolbarBoxToolbar-9D9A3855882D4AA792E941334DB770AE\" style=\"left: 0px; top: 0px; width: 714px; height: 30px;\"><div class=\"bk-toolbar-above bk-plot-above bk-toolbar-not-sticky bk-toolbar-active\">\n  \n  <a href=\"http://bokeh.pydata.org/\" target=\"_blank\" class=\"bk-logo bk-logo-small\"></a>\n  \n  <div class=\"bk-button-bar\">\n    <ul class=\"bk-button-bar-list\" type=\"pan\"><li><button type=\"button\" class=\"bk-toolbar-button hover active\">\n  <div class=\"bk-btn-icon bk-tool-icon-pan\"></div>\n  <span class=\"tip\">Pan</span>\n</button>\n</li><li><button type=\"button\" class=\"bk-toolbar-button hover\">\n  <div class=\"bk-btn-icon bk-tool-icon-box-zoom\"></div>\n  <span class=\"tip\">Box Zoom</span>\n</button>\n</li></ul>\n    <ul class=\"bk-button-bar-list\" type=\"scroll\"><li><button type=\"button\" class=\"bk-toolbar-button hover\">\n  <div class=\"bk-btn-icon bk-tool-icon-wheel-zoom\"></div>\n  <span class=\"tip\">Wheel Zoom</span>\n</button>\n</li></ul>\n    <ul class=\"bk-button-bar-list\" type=\"pinch\"></ul>\n    <ul class=\"bk-button-bar-list\" type=\"tap\"></ul>\n    <ul class=\"bk-button-bar-list\" type=\"press\"></ul>\n    <ul class=\"bk-button-bar-list\" type=\"rotate\"></ul>\n    <ul class=\"bk-button-bar-list\" type=\"actions\"><li><button type=\"button\" class=\"bk-toolbar-button hover\">\n  <div class=\"bk-btn-icon bk-tool-icon-save\"></div>\n  <span class=\"tip\">Save</span>\n</button>\n</li><li><button type=\"button\" class=\"bk-toolbar-button hover\">\n  <div class=\"bk-btn-icon bk-tool-icon-reset\"></div>\n  <span class=\"tip\">Reset</span>\n</button>\n</li></ul>\n    <div class=\"bk-button-bar-list bk-bs-dropdown\" type=\"inspectors\" style=\"display: none;\"></div>\n    <ul class=\"bk-button-bar-list\" type=\"help\"><li><button type=\"button\" class=\"bk-toolbar-button hover\">\n  <div class=\"bk-btn-icon bk-tool-icon-help\"></div>\n  <span class=\"tip\">Click the question mark to learn more about Bokeh plot tools.</span>\n</button>\n</li></ul>\n  </div>\n</div>\n</div></div><div class=\"bk-grid-column bk-layout-fixed\" id=\"modelid_c4368e48-e68c-4151-84b6-81e604d3d419\" style=\"width: 714px; height: 534px;\"><div class=\"bk-grid-row bk-layout-fixed\" id=\"modelid_048b8302-8eb7-4259-b102-6ccf8c2be5ac\" style=\"width: 714px; height: 534px;\"><div class=\"bk-plot-layout bk-layout-fixed\" id=\"modelid_633f4482-22b7-4360-ae4f-673048c8c8b5\" style=\"width: 714px; height: 534px;\"><div class=\"bk-plot-wrapper\" style=\"position: absolute; left: 0px; top: 0px; width: 714px; height: 534px;\"><div class=\"bk-canvas-wrapper\" style=\"touch-action: none; user-select: none; -webkit-user-drag: none; -webkit-tap-highlight-color: rgba(0, 0, 0, 0); width: 714px; height: 534px;\">\n<div class=\"bk-canvas-events\"></div>\n<div class=\"bk-canvas-overlays\"><div class=\"bk-shading\" style=\"display: none;\"></div></div>\n<canvas class=\"bk-canvas\" width=\"1785\" height=\"1335\" style=\"width: 714px; height: 534px;\"></canvas></div></div></div></div></div></div>\n    </div>\n<script type=\"text/javascript\">\n  \n  (function(global) {\n    function now() {\n      return new Date();\n    }\n  \n    var force = \"\";\n  \n    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force !== \"\") {\n      window._bokeh_onload_callbacks = [];\n      window._bokeh_is_loading = undefined;\n    }\n  \n  \n    \n    if (typeof (window._bokeh_timeout) === \"undefined\" || force !== \"\") {\n      window._bokeh_timeout = Date.now() + 0;\n      window._bokeh_failed_load = false;\n    }\n  \n    var NB_LOAD_WARNING = {'data': {'text/html':\n       \"<div style='background-color: #fdd'>\\n\"+\n       \"<p>\\n\"+\n       \"BokehJS does not appear to have successfully loaded. 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        {
            "source": "### Visualize correlations ####\n - Useful for datasets with small number of columns\n - A heatmap is useful to visualize how variables are related to each other (and not just the target)\n - Very useful while doing NLP applications to visualize similarity between documents (after using TFIDF)\n ", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 17, 
            "source": "import matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom pyspark.mllib.stat import Statistics\n\nplt.style.use('ggplot')\nvalues = data.rdd.map(lambda x:list(x.asDict().values()))\ncorr_values = Statistics.corr(values)\nnames = data.rdd.map(lambda x:list(x.asDict().keys())).first()\n\nplt.figure(figsize=(10,10))\nsns.set_style(\"darkgrid\")\nsns.heatmap(corr_values,xticklabels=names,yticklabels=names,square=True,vmin=0, vmax=1,cmap=\"YlGnBu\")", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "name": "stderr", 
                    "output_type": "stream", 
                    "text": "/gpfs/fs01/user/s280-a46e86a84e2274-95b1885704d4/.local/lib/python2.7/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family [u'sans-serif'] not found. Falling back to DejaVu Sans\n  (prop.get_family(), self.defaultFamily[fontext]))\n"
                }, 
                {
                    "execution_count": 17, 
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b90722590>"
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T/+rcrESxyuRvp2KF8nqyhUJ57+EPhPIXbhsL5Ts0LxPK6/j1FcoPeEtfKJ9fLvZ/2Utf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\nUGwCAADgZf4AY4T5r3qyQ+kAAAAASUVORK5CYII=\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7f2b90fb1090>"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ]
        }, 
        {
            "execution_count": 24, 
            "source": "!pip install regex", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Requirement already satisfied: regex in /gpfs/global_fs01/sym_shared/YPProdSpark/user/s280-a46e86a84e2274-95b1885704d4/.local/lib/python2.7/site-packages\r\n"
                }
            ]
        }, 
        {
            "source": "## We are all set to do some machine learning!\n\n<center><img width=\"350px\" height=\"350px\" margin=\"auto\" src=\"http://3.bp.blogspot.com/-SITensUIuVk/VXH6MA7GLJI/AAAAAAAACno/HjrhPkEjT04/s1600/depositphotos_49850487-Cartoon-paleontologist.jpg\"></center>", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "## Module layout\n##### It divides into two packages:\n- spark.mllib contains the original API built on top of RDDs.\n- spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines.\n- Using spark.ml is recommended because with DataFrames the API is more versatile and flexible. But we will keep supporting spark.mllib along with the development of spark.ml. Users should be comfortable using spark.mllib features and expect more features coming.\n\n    \n    \n#### A common receipe\n- Make sure your data is numeric \n- Collect feature columns\n- Define transformations (Vector Indexer, Encoder)\n- Assemble columns into a single Vector column (VectorAssembler)\n- Split data into test and train\n- Define pipelines and estimators", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "### Machine learning Pipelines\n- Easy to use API\n- Define stages to transform your dataset\n- Select any estimator (model) for prediction \n- Each pipeline will model a single estimator onto your model\n\n\n<center><img src=\"https://image.slidesharecdn.com/mlwithapachespark-2-160818115257/95/introduction-to-ml-with-apache-spark-mllib-47-638.jpg?cb=1490306278\" width=\"500px\" height=\"500px\"></center>", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "### What are we trying to again???", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "### PCA  - Dimensionality Reduction \n - Very handy for visualization!", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 45, 
            "source": "###### PCA Visualize data ######\nfrom pyspark.ml.feature import PCA\nfrom pyspark.ml.linalg import Vectors\nfrom pyspark.sql.functions import UserDefinedFunction\nfrom pyspark.ml.feature import VectorAssembler \n\nfeature_cols = list(filter(lambda x:x not in ['id','label','dummy'],data.columns))\nassembler = VectorAssembler(inputCols=feature_cols,outputCol='features')\ndf = assembler.transform(data)\n\npca = PCA(k=2, inputCol=\"features\", outputCol=\"pcaFeatures\")\nmodel = pca.fit(df)\n\npca_result = model.transform(df)\nresult = pca_result.select(\"pcaFeatures\")", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "execution_count": 38, 
            "source": "import regex\n\ndef g(point,co_ord):\n    if co_ord=='x':\n        value = regex.split('\\s+',point)[0][1:]\n    else:\n        value =  regex.split('\\s+',point)[1][:-1]\n    return value\n\nget_x_coord = UserDefinedFunction(lambda point:g(point,'x'), StringType())\nget_y_coord = UserDefinedFunction(lambda point:g(point,'y'), StringType())\nconvert_to_string = UserDefinedFunction(lambda point:str(point.toArray()),StringType())\n\npca_result = pca_result.withColumn('pcaFeatures_string',convert_to_string(pca_result['pcaFeatures']))\npca_result = pca_result.withColumn('x_coord',get_x_coord(pca_result['pcaFeatures_string']))\\\n                       .withColumn('y_coord',get_y_coord(pca_result['pcaFeatures_string']))\\\n                      \npca_result = pca_result.withColumn('x_coord',pca_result.x_coord.cast(FloatType()))\\\n                       .withColumn('y_coord',pca_result.y_coord.cast(FloatType()))\n    \nviz_df = pca_result.select('x_coord','y_coord','label').toPandas()\na = sns.lmplot(x='x_coord',y='y_coord',hue='label',data=viz_df,\n           fit_reg=False,palette={1:'red',0:'green'},size=6,aspect=1)", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "data": {
                        "image/png": 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ZIiNjIxDovmaaGM1N5Dx4H81xbRtscJ+dN4e903b3GJFmuDIiz5m/s45rd4Q4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\nMW3aNLjd7jboAFF4YJAStZFevXohPz8fixYtwu3bt7F06VK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
                        "text/plain": "<matplotlib.figure.Figure at 0x7f6a9d3e4a10>"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ]
        }, 
        {
            "source": "### Prepare the dataset for machine learning\n    - Split data between test and train (70/30 split)\n    - Apply logistic regression and decision tree classifiers", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "#### Convert 'label' to DoubleType()\n", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 39, 
            "source": "data = data.withColumn('label',data['label'].cast(DoubleType()))", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "source": "#### Split dataset into test and train sets (70/30) splits", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 46, 
            "source": "train, test = data.randomSplit([0.7,0.3])", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "source": "#### Count number of records in test and train sets", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 23, 
            "source": "train.count(), test.count()", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "execution_count": 23, 
                    "data": {
                        "text/plain": "(405, 164)"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ]
        }, 
        {
            "source": "#### Define Assembler to combine feature into a single vetor (feature vector! :) )", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 41, 
            "source": "from pyspark.ml.pipeline import Pipeline\nfrom pyspark.ml.linalg import Vectors #linear algebra package, has matrices, arrays, Vectors (dense and sparse)\nfrom pyspark.ml.feature import VectorAssembler \n\nfeature_cols = list(filter(lambda x:x not in ['id','label'],data.columns))\nassembler = VectorAssembler(inputCols=feature_cols,outputCol='features')", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "source": "## Classification Pipelines \n- Logistic Regression\n- Tree based classifier\n\n<center><img src=\"https://codesachin.files.wordpress.com/2015/08/linearly_separable_4.png\"></center>", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "source": "### Tree classifier ###", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 42, 
            "source": "from pyspark.ml.classification import DecisionTreeClassifier\n# define estimator and fit data\nestimator = DecisionTreeClassifier()\npipeline = Pipeline(stages=[assembler,estimator])\ntree_model = pipeline.fit(train)\n\n# get results\ndt_results = tree_model.transform(test)", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "source": "### Vizualise results of a tree based classifier", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": null, 
            "source": "### Visualize the results of tree classifier ###\npca_subset = pca_result.select('ID','features','x_coord','y_coord')\nviz_df = pca_subset.join(results,on=['ID','features']).select('x_coord','y_coord','prediction','label')\nviz_df = viz_df.dropna(how='any',subset=['x_coord','y_coord']).toPandas()\n\na = sns.lmplot(x='x_coord',y='y_coord',hue='prediction',col='label',data=viz_df,\n           fit_reg=False,palette={1:'red',0:'green'},size=6,aspect=1)", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "data": {
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OQPf0iRnP9957Ty+++KL+67/+S5deeqkk6fbbb9f8+fN122236dlnn1VWVpYeeeQRSdLk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\nqry8XNu3b5fX69Xq1as1ZcqUNtdMmTJ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4WJu9X48e97bZe3V01Cow1Clw1Cow1KlttGX/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\nQwsoGWJwAAAAAElFTkSuQmCC\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7f6a9d5bd1d0>"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ]
        }, 
        {
            "source": "##### Logistic regression #####", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 47, 
            "source": "from pyspark.ml.classification import LogisticRegression\n\n#define estimator and fit data\nestimator = LogisticRegression()\npipeline = Pipeline(stages=[assembler,estimator])\nlr_model = pipeline.fit(train)\n\n#get results\nlr_results = lr_model.transform(test)", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "execution_count": 48, 
            "source": "lr_results.select('id','features','label','probability','prediction').show(5)", 
            "metadata": {
                "scrolled": true
            }, 
            "cell_type": "code", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "+-----+--------------------+-----+--------------------+----------+\n|   id|            features|label|         probability|prediction|\n+-----+--------------------+-----+--------------------+----------+\n|85715|[13.1700000762939...|  1.0|           [0.0,1.0]|       1.0|\n|86355|[22.2700004577636...|  1.0|           [0.0,1.0]|       1.0|\n|86409|[14.2600002288818...|  0.0|[1.0,1.6359028295...|       0.0|\n|86517|[18.6599998474121...|  1.0|           [0.0,1.0]|       1.0|\n|87163|[13.4300003051757...|  1.0|           [0.0,1.0]|       1.0|\n+-----+--------------------+-----+--------------------+----------+\nonly showing top 5 rows\n\n"
                }
            ]
        }, 
        {
            "execution_count": 31, 
            "source": "lr_results.select('probability','prediction','label').take(3)", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "execution_count": 31, 
                    "data": {
                        "text/plain": "[Row(probability=DenseVector([0.0, 1.0]), prediction=1.0, label=1.0),\n Row(probability=DenseVector([0.0, 1.0]), prediction=1.0, label=1.0),\n Row(probability=DenseVector([1.0, 0.0]), prediction=0.0, label=0.0)]"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ]
        }, 
        {
            "execution_count": 50, 
            "source": "### Visualize results from logistic regression ###\npca_subset = pca_result.select('id','features','x_coord','y_coord')\nviz_df = pca_subset.join(lr_results,on=['id','features']).select('x_coord','y_coord','prediction','label')\nviz_df = viz_df.dropna(how='any',subset=['x_coord','y_coord']).toPandas()\n\na = sns.lmplot(x='x_coord',y='y_coord',hue='prediction',col='label',data=viz_df,\n           fit_reg=False,palette={1:'red',0:'green'},size=6,aspect=1)\na.set_titles(col_template=['Decision Tree results when label==\"Benign\"','Decision Tree results when label==\"Malignant\"'])", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "execution_count": 50, 
                    "data": {
                        "text/plain": "<seaborn.axisgrid.FacetGrid at 0x7f6a9d0cb850>"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }, 
                {
                    "data": {
                        "image/png": 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dl0axQ7HDHU9jx/Hjx0us63PLZjKZGDJkCEOGDOHYsWOMHDmSDz74gCeeeKLY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\nQYPK/5uR0ehT3nXXXVq5cmWtj2/btk3Z2dnKyMjQ/PnzNXfu3EY/Z4V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GOAcAAAAAwGKE\ncwAAAAAALOa0ugMAAABASxeyJVOhb7wux+Fs+eJ7qnj8RJUmJlvdLQBtCOEcAAAAqEPIlkx1XDgv\n8L0jK0sdF85ToURABxA0DGsHAAAA6hD6xus1t69d08w9AdCWEc4BAACAOjgOZzeoHQAuBeEcAAAA\nqIMvvmeD2gHgUhDOAQAAgDoUj59Yc/u4Cc3cEwBtGQvCAQAAAHUoTUxWocrnmAdWax83gcXgAAQV\n4RwAAACoR2liMmEcQJNiWDsAAAAAABYjnAMAAAAAYDHCeZCsWrVKV111lU6fPi1JMgxDCxYsUEpK\nitLS0nTgwIHAsRs2bFBqaqpSU1O1YcMGq7oMAAAAAGghmHMeBCdOnND//M//qGvXroG2bdu2KTs7\nWxkZGfr44481d+5crVu3Tnl5efrtb3+rt99+WzabTXfddZeSkpIUFRVl4SsAAAAAAFiJO+dB8Pzz\nz+uJJ56QzWYLtGVmZmr06NGy2Wzq37+/CgoKlJubqx07duimm25SdHS0oqKidNNNN2n79u0W9h4A\nAAAAYDXunDfSBx98oNjYWPXu3btKu8fjUVxcXOD7uLg4eTyeau1ut1sej6fe5+nUKVxOpyNo/Y6J\niQjaudoy6mQOdTKPWplDnYKDa4c1qJN51Moc6mQOdUJrRzg3YdKkSfr222+rtc+YMUPLly/XqlWr\nmrwPZ84UBe1cMTEROnnybNDO11ZRJ3Ook3nUyhzqVD+zb0C5djQ/6mQetTKHOplDnerHhxctH+Hc\nhFdffbXG9i+++ELffPONRo0aJUnKycnRXXfdpXXr1sntdisnJydwbE5Ojtxut9xut3bt2hVo93g8\nGjRoUJP2HwAAAADQsjHnvBGuuuoqffTRR9q8ebM2b96suLg4rV+/XjExMUpKStLGjRtlGIb27dun\niIgIxcbGasiQIdqxY4fy8/OVn5+vHTt2aMiQIVa/FAAAAACAhbhz3kSGDh2qDz/8UCkpKQoLC9PC\nhQslSdHR0Xr44Yc1duxYSdIjjzyi6OhoK7sKAAAAALAY4TyINm/eHPjaZrNpzpw5NR43duzYQDgH\nAAAAAIBh7QAAAAAAWMxmGIZhdScAAAAAAGjPuHMOAAAAAIDFCOcAAAAAAFiMcA4AAAAAgMUI5wAA\nAAAAWIxwDgAAAACAxQjnAAAAAABYjHAOAAAAAIDFCOdtzLJly/TjH/9Yo0aN0qhRo/Thhx8GHlu+\nfLlSUlI0fPhwbd++PdC+bds2DR8+XCkpKVqxYkWg/ejRo7r77ruVkpKiGTNmyOv1NutraS6rVq3S\nVVddpdOnT0uSDMPQggULlJKSorS0NB04cCBw7IYNG5SamqrU1FRt2LAh0P5///d/SktLU0pKihYs\nWCDDMJr9dTSVpUuXKi0tTaNGjdKUKVPk8XgkUaeL/epXv9Ktt96qtLQ0PfLIIyooKAg8xu9eVX/7\n2980YsQI9e7dW5988kmVx6iVNbh2NBzXjrpx7TCHa4d5XDvQLhhoU37zm98YK1eurNb+1VdfGWlp\naUZJSYlx5MgRIzk52SgrKzPKysqM5ORk48iRI0ZJSYmRlpZmfPXVV4ZhGMa0adOMd955xzAMw3jm\nmWeMP/3pT836WprD8ePHjSlTphi33HKLcerUKcMwDGPr1q3GfffdZ/j9fmPv3r3G2LFjDcMwjDNn\nzhhJSUnGmTNnjLy8PCMpKcnIy8szDMMwxowZY+zdu9fw+/3GfffdZ2zdutWy1xRsZ8+eDXz92muv\nGc8884xhGNTpYtu3bzdKS0sNwzCMF154wXjhhRcMw+B3ryYHDx40vv76a2PChAnG/v37A+3Uyjpc\nOxqGa0f9uHaYw7XDPK4daA+4c95OZGZmasSIEXK5XOrevbvi4+O1f/9+7d+/X/Hx8erevbtcLpdG\njBihzMxMGYah//3f/9Xw4cMlSXfeeacyMzMtfhXB9/zzz+uJJ56QzWYLtGVmZmr06NGy2Wzq37+/\nCgoKlJubqx07duimm25SdHS0oqKidNNNN2n79u3Kzc3VuXPn1L9/f9lsNo0ePbpN1eqyyy4LfH3+\n/PlArahTVUOGDJHT6ZQk9e/fXzk5OZL43avJlVdeqYSEhGrt1Krl4f9Jzbh21I9rhzlcO8zj2oH2\ngHDeBv3pT39SWlqann76aeXn50uSPB6P4uLiAse43W55PJ5a28+cOaPIyMjABSMuLi4wJK2t+OCD\nDxQbG6vevXtXab+4JhWv3WwN22KtlixZoqFDh2rTpk2aPn26JOpUl7fffls333yzJH73GoJaWYtr\nhzlcO8zj2tEwXDsuDbVCW+K0ugNouEmTJunbb7+t1j5jxgyNHz9eDz/8sGw2m1588UX98pe/1PPP\nP29BL1uGumq1fPlyrVq1yoJetTx11WnYsGGaOXOmZs6cqeXLl2vNmjWaNm2aBb20Xn11kqSXX35Z\nDodDd9xxR3N3r0UxUys0L64d5nHtMIdrhzlcO8zj2oH2jnDeCr366qumjrv77rv14IMPSir/tLBi\nqJRU/imj2+2WpBrbO3XqpIKCApWVlcnpdConJydwfGtSW62++OILffPNNxo1apSk8hrcddddWrdu\nXbVaVbx2t9utXbt2Bdo9Ho8GDRpU6/Gtidl/U2lpaZo6daqmTZtGnWqwfv16bd26Va+++mpgCCe/\ne+a111o1F64d5nHtMIdrhzlcO8zj2oH2jmHtbUxubm7g6w8++EC9evWSJCUlJSk9PV1er1dHjx5V\ndna2+vbtqz59+ig7O1tHjx6V1+tVenq6kpKSZLPZdMMNN+i9996TVL6CalJSkiWvqSlcddVV+uij\nj7R582Zt3rxZcXFxWr9+vWJiYpSUlKSNGzfKMAzt27dPERERio2N1ZAhQ7Rjxw7l5+crPz9fO3bs\n0JAhQxQbG6vLLrtM+/btk2EY2rhxo5KTk61+iUGTnZ0d+DozMzMw34s6VbVt2zatXLlSL7/8ssLC\nwgLt/O6ZR62sw7XDHK4d5nHtMIdrR+NRK7QpVqxCh6bz+OOPGyNHjjRGjhxpPPDAA4bH4wk89tJL\nLxnJyclGampqlZVOt27daqSmphrJycnGSy+9FGg/cuSIMWbMGGPYsGHGo48+apSUlDTra2lOiYmJ\ngRV3/X6/MXfuXCM5OdkYOXJklRVB161bZwwbNswYNmyY8dZbbwXa9+/fb4wYMcJITk42nn32WcPv\n9zf7a2gqP/vZz4wRI0YE/k3l5OQYhkGdLjZs2DDj5ptvNu644w7jjjvuCKxMbBj87l0sIyPD+PGP\nf2xcc801xuDBg40pU6YEHqNW1uDacWm4dtSOa4c5XDvM49qB9sBmGG1os0gAAAAAAFohhrUDAAAA\nAGAxwjkAAAAAABYjnAMAAAAAYDHCOQAAAAAAFiOcAwAAAABgMcI5AAAAAAAWI5wDaLeWLVumxx9/\n3OpuAABaGa4fAJoC4RwAAAAAAIsRzgG0eYZhyO/3W90NAEArw/UDQHMinAMIqiNHjmjQoEE6cOCA\nJMnj8ehHP/qRdu7cWevP5OXl6emnn9aQIUM0cOBAPfzww4HH3nzzTaWkpGjQoEF68MEH5fF4Ao/t\n2bNHY8aM0XXXXacxY8Zoz549gccmTpyoJUuWaNy4cerXr5+OHj2qo0ePasKECRowYIAmT56sM2fO\nNEEFAACXgusHgPaOcA4gqHr06KHHH39cTzzxhM6fP69Zs2bpzjvv1A033FDrzzz55JM6f/680tPT\n9fe//12TJk2SJH300UdatGiRli5dqh07dqhbt2567LHHJJW/IXvggQc0ceJE7dy5U5MnT9YDDzxQ\n5Q3TX/7yF82fP1979uxR165d9fjjj+uaa67Rzp079fDDD2vDhg1NWgsAgHlcPwC0d4RzAEF3zz33\nqEePHrrnnnuUm5urmTNn1npsbm6utm3bpmeffVZRUVEKCQnRoEGDJEmbNm3SmDFjdM0118jlcumx\nxx7Tvn379M0332jr1q2Kj4/X6NGj5XQ6NXLkSCUkJGjLli2Bc995553q1auXnE6nTp48qU8++UTT\np0+Xy+XSwIEDlZSU1OS1AACYx/UDQHtGOAfQJO655x59+eWXmjhxolwuV63H5eTkKCoqSlFRUdUe\ny83NVbdu3QLfd+zYUdHR0fJ4PMrNzVXXrl2rHN+1a9cqwxa7dOlS5VyRkZEKDw+vcjwAoGXh+gGg\nvSKcAwi6wsJCLVy4UGPHjtWyZcuUl5dX67FxcXHKz89XQUFBtcdiY2N17NixwPdFRUXKy8uT2+1W\nbGysjh8/XuX4EydOyO12B7632WyBr2NiYlRQUKCioqJA28U/DwCwFtcPAO0Z4RxA0D333HO69tpr\n9dxzz+mWW27RnDlzaj02NjZWN998s5599lnl5+ertLRU//jHPyRJI0eO1Pr16/XZZ5/J6/Vq8eLF\n6tu3r6644goNHTpU2dnZ2rRpk8rKyvTuu+/q4MGDuuWWW2p8nm7duunaa6/VsmXL5PV6tXv37ipD\nGAEA1uP6AaA9I5wDCKoPPvhA27dv19y5cyVJTz31lD799FP99a9/rfVnXnjhBTmdTt1222268cYb\n9dprr0mSbrzxRk2fPl2PPvqohgwZoqNHj2rJkiWSpE6dOumVV17R6tWrdcMNN2jlypV65ZVXdPnl\nl9f6PIsWLdLHH3+sG264Qb/73e80evTo4L1wAECjcP0A0N7ZDMMwrO4EAAAAAADtGXfOAQAAAACw\nmNPqDgBoHwYMGFBj++9//3tdf/31zdwbAEBrwfUDQHvBsHYAAAAAACzGsHYAAAAAACxGOAcAAAAA\nwGKEcwAAAAAALEY4BwAAAADAYv8f06Qw4iI/KPwAAAAASUVORK5CYII=\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7f6ab7622910>"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ]
        }, 
        {
            "source": "### Evaluation\n\n- Classification models work towards making a Yes/No decision\n- Metrics to evaluate the model: Precision/Recall, AUC\n\n\n<center><img width=\"500px\" height=\"500px\" src=\"http://opexanalytics.com/cnt/uploads/2016/01/Red-Fish-High-Recall.jpg\"></center>", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 49, 
            "source": "#### Classification metrics ####\ndef calc_metrics(results):\n    metrics = {}\n    metrics['tp_0'] = results.filter((results.label==0)&(results.prediction==0)).count()\n    metrics['fn_0'] = results.filter((results.label==0)&(results.prediction==1)).count()\n    metrics['tn_0'] = results.filter((results.label==1)&(results.prediction==1)).count()\n    metrics['fp_0'] = results.filter((results.label==1)&(results.prediction==0)).count()\n    \n    metrics['tp_1'] = results.filter((results.label==1)&(results.prediction==1)).count()\n    metrics['fn_1'] = results.filter((results.label==1)&(results.prediction==0)).count()\n    metrics['tn_1'] = results.filter((results.label==0)&(results.prediction==0)).count()\n    metrics['fp_1'] = results.filter((results.label==0)&(results.prediction==1)).count()\n    \n    return metrics\n        \n### calc precision & recall ###\ndef precision_recall(results):\n    items = calc_metrics(results)\n    rows = []\n    \n    row = {'class':0,'precision':items['tp_0']/float((items['tp_0']+items['fp_0'])+1),'recall':items['tp_0']/float((items['tp_0']+items['fn_0'])+1)}\n    rows.append(row)\n    row = {'class':1,'precision':items['tp_1']/float((items['tp_1']+items['fp_1'])+1),'recall':items['tp_1']/float((items['tp_1']+items['fn_1'])+1)}\n    rows.append(row)\n    \n    return rows", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "source": "#### Decision Tree metrics ", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 54, 
            "source": "pd.DataFrame(precision_recall(dt_results))", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "execution_count": 54, 
                    "data": {
                        "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>class</th>\n      <th>precision</th>\n      <th>recall</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0.880000</td>\n      <td>0.897959</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0.848485</td>\n      <td>0.823529</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "   class  precision    recall\n0      0   0.880000  0.897959\n1      1   0.848485  0.823529"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ]
        }, 
        {
            "source": "#### Logistic regression metrics ", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 53, 
            "source": "pd.DataFrame(precision_recall(lr_results))", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "execution_count": 53, 
                    "data": {
                        "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>class</th>\n      <th>precision</th>\n      <th>recall</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0.950000</td>\n      <td>0.957983</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0.932432</td>\n      <td>0.920000</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "   class  precision    recall\n0      0   0.950000  0.957983\n1      1   0.932432  0.920000"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ]
        }, 
        {
            "source": "### Clustering Pipeline\n- K-means clustering", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 39, 
            "source": "######## Kmeans clustering, join to the PCA set #########\nfrom numpy import array\nfrom math import sqrt\nfrom pyspark.ml.clustering import KMeans\n\nkmeans = KMeans(k=2,featuresCol='features')\nmodel = kmeans.fit(pca_result)\ncenters = model.clusterCenters()\nkmeans_result = model.transform(pca_result).select('id','features','prediction')\n\nviz_df = pca_result.join(kmeans_result,on=['id','features'],how='inner')\\\n                   .select('id','features','x_coord','y_coord','prediction','label')\n\nviz_df = viz_df.dropna(how='any',subset=['x_coord','y_coord']).toPandas()\n\nsns.lmplot(x='x_coord',y='y_coord',hue='prediction',col='label',data=viz_df,\n           fit_reg=False,palette={1:'red',0:'green'},size=6,aspect=1)", 
            "metadata": {
                "scrolled": true
            }, 
            "cell_type": "code", 
            "outputs": [
                {
                    "execution_count": 39, 
                    "data": {
                        "text/plain": "<seaborn.axisgrid.FacetGrid at 0x7fc47c047190>"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }, 
                {
                    "data": {
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h0ElERERERERxw6CTiIiIiIiI4oZBJxEREREREcUNg04iIiIiIiKKGwadREREREREFDcM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\nAAAAWIakEwAAAABgGZJOAAAAAIBl/j9I0sMGCgrVjgAAAABJRU5ErkJggg==\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7fc47c047710>"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ]
        }, 
        {
            "source": "### Deploy to Cloud by using Watson Machine Learning Repo\n\n- Deploy your model with a few lines of code\n- Provides a link that can be embedded into applications (easy hand-off from data scientists to developers)\n\nIBM WML Reference: http://watson-ml-api.mybluemix.net/#/Online32Scoring\n", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "markdown"
        }, 
        {
            "source": "### Step 1: Setup credentials ###", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 79, 
            "source": "# The code was removed by DSX for sharing.", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "source": "### Step 2: Save model ### ", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 80, 
            "source": "### Required imports ###\nimport urllib3, requests, json\n\n### Authenticate ML repo ###\nfrom repository.mlrepositoryclient import MLRepositoryClient\nfrom repository.mlrepositoryartifact import MLRepositoryArtifact\n\nml_repository_client = MLRepositoryClient(service_path)\nml_repository_client.authorize(username, password)\n\n### Save model ###\nmodel_artifact_lr = MLRepositoryArtifact(lr_model, training_data=train, name=\"Logistic Regression Classifer\")\nsaved_model = ml_repository_client.models.save(model_artifact_lr)", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "source": "### Load saved model and make predictions (batch) ###", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 81, 
            "source": "#### load model ###\nloadedModelArtifact = ml_repository_client.models.get(saved_model.uid)\n\n### make predictions ####\npredictions = loadedModelArtifact.model_instance().transform(test)\nprecision_recall(predictions)", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "execution_count": 81, 
                    "data": {
                        "text/plain": "{'pre_0': 0.9907407407407407,\n 'pre_1': 1.0,\n 'recall_0': 1.0,\n 'recall_1': 0.9836065573770492}"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ]
        }, 
        {
            "source": "### Obtain token to interact with REST API ###", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 55, 
            "source": "### get token ###\nheaders = urllib3.util.make_headers(basic_auth='{}:{}'.format(username, password))\nurl = '{}/v2/identity/token'.format(service_path)\nresponse = requests.get(url, headers=headers)\nmltoken = json.loads(response.text).get('token')", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "source": "### Deploy model ###", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 53, 
            "source": "### deploy model ###\nendpoint_online = service_path + \"/v2/online/deployments/\"\nheader_online = {'Content-Type': 'application/json', 'Authorization': mltoken}\npayload_online = {\"artifactVersionHref\": saved_model.meta.prop(\"modelVersionHref\"), \"name\": \"Product Line Prediction\"}\n\nresponse_online = requests.post(endpoint_online, json=payload_online, headers=header_online)\nscoring_href = json.loads(response_online.text).get('entity').get('scoringHref') ## this is the endpoint used for online scoring", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "source": "### Get list of existing models, obtain scoring endpoint for the desired model ###", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 114, 
            "source": "### get a list of existing deployments ###\nurl = service_path + \"/v2/online/deployments\"\nheader_online = {'Content-Type': 'application/json', 'Authorization': mltoken}\n\n# get scoring endpoint for this model\nresp = requests.get(url,headers=header_online)\nscoring_href = resp.json()['resources'][0]['entity']['scoringHref']", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "source": "### Get predictions from model (online predictions) ###", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 115, 
            "source": "### example of getting prediction with online scoring ####\ntest_row = test.take(5)[3]\ntest_features = [test_row[col] for col in test.columns if col not in ['label']]\n\n# get prediction from online scoring endpoint (scoring_href from above)\npayload_scoring = {\"record\":test_features}\nresponse_scoring = requests.put(scoring_href, json=payload_scoring, headers=header_online)\n", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }, 
        {
            "execution_count": 118, 
            "source": "from IPython.display import display\n\noutput = response_scoring.json()\nprint ('Output from scoring endpoint')\ndisplay(output)", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Output from scoring endpoint\n"
                }, 
                {
                    "data": {
                        "text/plain": "{u'result': {u'AREA_MEAN': 1264.0,\n  u'AREA_SE': 87.87000274658203,\n  u'AREA_WORST': 1750.0,\n  u'COMPACTNESS_MEAN': 0.13130000233650208,\n  u'COMPACTNESS_SE': 0.03482000157237053,\n  u'COMPACTNESS_WORST': 0.3538999855518341,\n  u'CONCAVE POINTS_MEAN': 0.08682999759912491,\n  u'CONCAVE POINTS_SE': 0.012690000236034393,\n  u'CONCAVE POINTS_WORST': 0.15729999542236328,\n  u'CONCAVITY_MEAN': 0.14650000631809235,\n  u'CONCAVITY_SE': 0.042319998145103455,\n  u'CONCAVITY_WORST': 0.4097999930381775,\n  u'FRACTAL_DIMENSION_SE': 0.0044109998270869255,\n  u'FRACTAL_DIMENSION_WORST': 0.08367999643087387,\n  u'ID': 86208,\n  u'PERIMETER_MEAN': 132.39999389648438,\n  u'PERIMETER_SE': 4.553999900817871,\n  u'PERIMETER_WORST': 156.10000610351562,\n  u'RADIUS_MEAN': 20.260000228881836,\n  u'RADIUS_SE': 0.7576000094413757,\n  u'RADIUS_WORST': 24.219999313354492,\n  u'SMOOTHNESS_MEAN': 0.09077999740839005,\n  u'SMOOTHNESS_WORST': 0.11900000274181366,\n  u'SYMMETRY_MEAN': 0.2094999998807907,\n  u'SYMMETRY_WORST': 0.36890000104904175,\n  u'TEXTURE_MEAN': 23.030000686645508,\n  u'TEXTURE_WORST': 31.59000015258789,\n  u'features': {u'values': [20.260000228881836,\n    23.030000686645508,\n    132.39999389648438,\n    1264.0,\n    0.09077999740839005,\n    0.13130000233650208,\n    0.14650000631809235,\n    0.08682999759912491,\n    0.2094999998807907,\n    0.7576000094413757,\n    4.553999900817871,\n    87.87000274658203,\n    0.03482000157237053,\n    0.042319998145103455,\n    0.012690000236034393,\n    0.0044109998270869255,\n    24.219999313354492,\n    31.59000015258789,\n    156.10000610351562,\n    1750.0,\n    0.11900000274181366,\n    0.3538999855518341,\n    0.4097999930381775,\n    0.15729999542236328,\n    0.36890000104904175,\n    0.08367999643087387]},\n  u'prediction': 1.0,\n  u'probability': {u'values': [1.413173287357375e-34, 1.0]},\n  u'rawPrediction': {u'values': [-77.94205542771127, 77.94205542771127]}}}"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ]
        }, 
        {
            "execution_count": 121, 
            "source": "print 'Prediction:',output['result']['prediction']\nprint 'Probability:',output['result']['probability']['values']\nprint 'RawPrediction:',output['result']['probability']['values']", 
            "metadata": {}, 
            "cell_type": "code", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Prediction: 1.0\nProbability: [1.413173287357375e-34, 1.0]\nRawPrediction: [1.413173287357375e-34, 1.0]\n"
                }
            ]
        }, 
        {
            "execution_count": null, 
            "source": "", 
            "metadata": {
                "collapsed": true
            }, 
            "cell_type": "code", 
            "outputs": []
        }
    ], 
    "nbformat": 4
}