{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Power generation is a complex process, and understanding and predicting power output is an important element in managing a plant and its connection to the power grid. The operators of a regional power grid create predictions of power demand based on historical information and environmental factors (e.g., temperature). They then compare the predictions against available resources (e.g., coal, natural gas, nuclear, solar, wind, hydro power plants). Power generation technologies such as solar and wind are highly dependent on environmental conditions, and all generation technologies are subject to planned and unplanned maintenance.\n", "\n", "Here is an real-world example of predicted demand (on two time scales), actual demand, and available resources from the California power grid: http://www.caiso.com/Pages/TodaysOutlook.aspx\n", "\n", "![](http://content.caiso.com/outlook/SP/ems_small.gif)\n", "\n", "The challenge for a power grid operator is how to handle a shortfall in available resources versus actual demand. There are three solutions to a power shortfall: build more base load power plants (this process can take many years to decades of planning and construction), buy and import power from other regional power grids (this choice can be very expensive and is limited by the power transmission interconnects between grids and the excess power available from other grids), or turn on small [Peaker or Peaking Power Plants](https://en.wikipedia.org/wiki/Peaking_power_plant). Because grid operators need to respond quickly to a power shortfall to avoid a power outage, grid operators rely on a combination of the last two choices. In this exercise, we'll focus on the last choice.\n", "\n", "Because they supply power only occasionally, the power supplied by a peaker power plant commands a much higher price per kilowatt hour than power from a power grid's base power plants. A peaker plant may operate many hours a day, or it may operate only a few hours per year, depending on the condition of the region's electrical grid. Because of the cost of building an efficient power plant, if a peaker plant is only going to be run for a short or highly variable time it does not make economic sense to make it as efficient as a base load power plant. In addition, the equipment and fuels used in base load plants are often unsuitable for use in peaker plants because the fluctuating conditions would severely strain the equipment.\n", "\n", "The power output of a peaker power plant varies depending on environmental conditions, so the business problem is _predicting the power output of a peaker power plant as a function of the environmental conditions_ -- since this would enable the grid operator to make economic tradeoffs about the number of peaker plants to turn on (or whether to buy expensive power from another grid)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem Definition\n", "\n", "Given the background information, we need to first perform Exploratory Data Analysis to understand the data and then translate the business problem (predicting power output as a function of envionmental conditions) into a Machine Learning task. In this instance, the ML task is regression since the label (or target) we are trying to predict is numeric. We will use an [Apache Spark ML Pipeline](https://spark.apache.org/docs/1.6.2/api/python/pyspark.ml.html#pyspark-ml-package) to perform the regression." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a DataFrame from the data\n", "\n", "The schema definition from UCI appears below:\n", "* AT = Atmospheric Temperature in C\n", "* V = Exhaust Vacuum Speed\n", "* AP = Atmospheric Pressure\n", "* RH = Relative Humidity\n", "* PE = Power Output. This is the value we are trying to predict given the measurements above." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "powerPlantDF = sqlContext.read.format(\"com.databricks.spark.csv\")\\\n", " .options(delimiter=',', header='true', inferschema='true')\\\n", " .load(\"data.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('AT', 'double'), ('V', 'double'), ('AP', 'double'), ('RH', 'double'), ('PE', 'double')]\n" ] } ], "source": [ "# Check the names and types of the columns\n", "print powerPlantDF.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore Data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Register powerPlantDF as an SQL table\n", "sqlContext.sql(\"DROP TABLE IF EXISTS power_plant\")\n", "sqlContext.registerDataFrameAsTable(powerPlantDF, \"power_plant\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-----+-----+-------+-----+------+\n", "| AT| V| AP| RH| PE|\n", "+-----+-----+-------+-----+------+\n", "| 8.34|40.77|1010.84|90.01|480.48|\n", "|23.64|58.49| 1011.4| 74.2|445.75|\n", "|29.74| 56.9|1007.15|41.91|438.76|\n", "|19.07|49.69|1007.22|76.79|453.09|\n", "| 11.8|40.66|1017.13| 97.2|464.43|\n", "+-----+-----+-------+-----+------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "sqlContext.sql(\"SELECT * FROM power_plant\").show(5)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+---------+-------+\n", "|col_name|data_type|comment|\n", "+--------+---------+-------+\n", "| AT| double| null|\n", "| V| double| null|\n", "| AP| double| null|\n", "| RH| double| null|\n", "| PE| double| null|\n", "+--------+---------+-------+\n", "\n" ] } ], "source": [ "sqlContext.sql(\"desc power_plant\").show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-------+------------------+------------------+------------------+------------------+-----------------+\n", "|summary| AT| V| AP| RH| PE|\n", "+-------+------------------+------------------+------------------+------------------+-----------------+\n", "| count| 9568| 9568| 9568| 9568| 9568|\n", "| mean|19.651231187291014|54.305803720735966|1013.2590781772483| 73.30897784280928|454.3650094063547|\n", "| stddev| 7.452473229611075|12.707892998326807| 5.938783705811638|14.600268756728957|17.06699499980342|\n", "| min| 1.81| 25.36| 992.89| 25.56| 420.26|\n", "| max| 37.11| 81.56| 1033.3| 100.16| 495.76|\n", "+-------+------------------+------------------+------------------+------------------+-----------------+\n", "\n" ] } ], "source": [ "powerPlantDF.describe().show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-----+-----+-------+-----+------+\n", "| AT| V| AP| RH| PE|\n", "+-----+-----+-------+-----+------+\n", "| 8.34|40.77|1010.84|90.01|480.48|\n", "|23.64|58.49| 1011.4| 74.2|445.75|\n", "|29.74| 56.9|1007.15|41.91|438.76|\n", "|19.07|49.69|1007.22|76.79|453.09|\n", "| 11.8|40.66|1017.13| 97.2|464.43|\n", "+-----+-----+-------+-----+------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "powerPlantDF.show(5)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pd_powerPlant = powerPlantDF.toPandas()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like there is strong linear correlation between Temperature and Power Output." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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RFBN+F21tIlYLSnRToBsMhtkF89hnMfr7+/Fnf/ZRtLc/A/GY5yFJaCsgXvwu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\n2gsQEbtzfSVzHGJbtEwHFudyJ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,6))\n", "plt.scatter(pd_powerPlant.PE, pd_powerPlant.AT)\n", "plt.xlabel('Power')\n", "plt.ylabel('Temprature')\n", "plt.title('Scatter of AT and PE');" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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sflwyla4beJbdu/ce6NFX5Oyzz0651yf9dsMwjMPAUFX+kXxhpvgDkvTnZjIF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\nqIfhGWAp0eS52+PtaZPnxhzHVLbYU5nZ5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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,6))\n", "plt.scatter(pd_powerPlant.PE, pd_powerPlant.V)\n", "plt.xlabel('Power')\n", "plt.ylabel('ExhaustVacuum')\n", "plt.title('Scatter of V and PE');" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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JhpZ8tUEhAkIEBIJFw/m+uPM02rmcf64CNV8ot8G9BOK4gcnJyXPW2t1Wvpx9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\nmOeuqMDGZ5SgBXMFxWIVBw8eMrEmvgWtDFvfgC0Q/TnHLyA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sFMXYCLGANrDp1vwmh0Y1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vXZAMiLrB6Hloc9C83JjuODm07ch+IkRp10Gjh2fONk3YHm7ukIsK6aIkPWYbG5yyE0wuqgrPZX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\nvbhRCPETNbbvCCHeau3b6eMI3Q8hxNXqXB8XQtxkbe/IcXjuxT3WffiRE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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,6))\n", "plt.scatter(pd_powerPlant.PE, pd_powerPlant.AP)\n", "plt.xlabel('Power')\n", "plt.ylabel('Pressure')\n", "plt.title('Scatter of AP and PE');" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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8aZJi14zVHssl141GIxw+/CFvfPZdsa2CWQPgoLONnrN6veO8A4eQJF3Mz794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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,6))\n", "plt.scatter(pd_powerPlant.PE, pd_powerPlant.RH)\n", "plt.xlabel('Power')\n", "plt.ylabel('Humidity')\n", "plt.title('Scatter of RH and PE');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Preparation\n", "\n", "The goal is to use machine learning to determine a function that yields the output power as a function of a set of predictor features. The first step in building our ML pipeline is to convert the predictor features from DataFrame columns to Feature Vectors using the ** pyspark.ml.feature.VectorAssembler() ** method.\n", "\n", "The VectorAssembler is a transformer that combines a given list of columns into a single vector column. It is useful for combining raw features and features generated by different feature transformers into a single feature vector, in order to train ML models like logistic regression and decision trees. VectorAssembler takes a list of input column names (each is a string) and the name of the output column (as a string)." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "VectorAssembler_4ceb80e267db095c6d46" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyspark.ml.feature import VectorAssembler\n", "\n", "vectorizer = VectorAssembler()\n", "vectorizer.setInputCols([\"AT\", \"V\", \"AP\", \"RH\"])\n", "vectorizer.setOutputCol(\"features\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Modeling\n", "\n", "Now let's model our data to predict what the power output will be given a set of sensor readings. Our first model will be based on simple linear regression since we saw some linear patterns in our data based on the scatter plots during the exploration stage.\n", "\n", "We need a way of evaluating how well our linear regression model predicts power output as a function of input parameters. We can do this by splitting up our initial data set into a Training Set used to train our model and a Test Set used to evaluate the model's performance in giving predictions. We can use a DataFrame's ** randomSplit() ** method to split our dataset. The method takes a list of weights and an optional random seed. The seed is used to initialize the random number generator used by the splitting function." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# We'll hold out 20% of our data for testing and leave 80% for training\n", "seed = 1800009193L\n", "(split20DF, split80DF) = powerPlantDF.randomSplit([0.2, 0.8], seed)\n", "\n", "testSetDF = split20DF\n", "trainingSetDF = split80DF" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Linear Regression Model" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pyspark.ml.regression import LinearRegression\n", "from pyspark.ml import Pipeline\n", "\n", "# Let's initialize our linear regression learner\n", "lr = LinearRegression()\n", "\n", "# Now we set the parameters for the method\n", "lr.setPredictionCol(\"Predicted_PE\")\\\n", " .setLabelCol(\"PE\")\\\n", " .setFeaturesCol(\"features\")\\\n", " .setMaxIter(100)\\\n", " .setRegParam(0.1)\n", " \n", "lrPipeline = Pipeline()\n", "lrPipeline.setStages([vectorizer, lr])\n", "\n", "# Let's train begins\n", "lrModel = lrPipeline.fit(trainingSetDF)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see what's this model telling us" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Linear Regression Equation: y = 427.913982218 - (1.90830649191 * AT) - (0.253812930071 * V) - (0.147465130103 * RH) + (0.0873935030455 * AP)\n" ] } ], "source": [ "# The intercept is as follows:\n", "intercept = lrModel.stages[1].intercept\n", "\n", "# The coefficents (i.e., weights) are as follows:\n", "weights = lrModel.stages[1].coefficients\n", "\n", "# Create a list of the column names (without PE)\n", "featuresNoLabel = [col for col in powerPlantDF.columns if col != \"PE\"]\n", "\n", "# Merge the weights and labels\n", "coefficents = zip(weights, featuresNoLabel)\n", "\n", "# Now let's sort the coefficients from greatest absolute weight most to the least absolute weight\n", "coefficents.sort(key=lambda tup: abs(tup[0]), reverse=True)\n", "\n", "equation = \"y = {intercept}\".format(intercept=intercept)\n", "variables = []\n", "for x in coefficents:\n", " weight = abs(x[0])\n", " name = x[1]\n", " symbol = \"+\" if (x[0] > 0) else \"-\"\n", " equation += (\" {} ({} * {})\".format(symbol, weight, name))\n", "\n", "# Here is our equation\n", "print(\"Linear Regression Equation: \" + equation)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+----+-----+-------+-----+------+------------------+\n", "| AT| V| AP| RH| PE| Predicted_PE|\n", "+----+-----+-------+-----+------+------------------+\n", "|1.81|39.42|1026.92|76.97|490.55| 492.8503868480957|\n", "| 3.2|41.31| 997.67|98.84|489.86|483.93681202706864|\n", "|3.38|41.31| 998.79|97.76|489.11| 483.8504599224484|\n", "| 3.4|39.64| 1011.1|83.43|459.86| 487.4251507227025|\n", "|3.51|35.47|1017.53|86.56|489.07|488.37401129434926|\n", "+----+-----+-------+-----+------+------------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "# Apply our LR model to the test data and predict power output\n", "predictionsAndLabelsDF = lrModel.transform(testSetDF).select(\"AT\", \"V\", \"AP\", \"RH\", \"PE\", \"Predicted_PE\")\n", "predictionsAndLabelsDF.show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "RSME is defined as follows: \\\\( RMSE = \\sqrt{\\frac{\\sum_{i = 1}^{n} (x_i - y_i)^2}{n}}\\\\) where \\\\(y_i\\\\) is the observed value and \\\\(x_i\\\\) is the predicted value\n", "\n", "RMSE is a frequently used measure of the differences between values predicted by a model or an estimator and the values actually observed. The lower the RMSE, the better our model." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Root Mean Squared Error: 4.61\n" ] } ], "source": [ "# Now let's compute an evaluation metric for our test dataset\n", "from pyspark.ml.evaluation import RegressionEvaluator\n", "\n", "# Create an RMSE evaluator using the label and predicted columns\n", "regEval = RegressionEvaluator(predictionCol=\"Predicted_PE\", labelCol=\"PE\", metricName=\"rmse\")\n", "\n", "# Run the evaluator on the DataFrame\n", "rmse = regEval.evaluate(predictionsAndLabelsDF)\n", "\n", "print(\"Root Mean Squared Error: %.2f\" % rmse)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another useful statistical evaluation metric is the ** coefficient of determination **, denoted \\\\(R^2\\\\) or \\\\(r^2\\\\) and pronounced ** \"R squared\" **. It is a number that indicates the proportion of the variance in the dependent variable that is predictable from the independent variable and it provides a measure of how well observed outcomes are replicated by the model, based on the proportion of total variation of outcomes explained by the model. The coefficient of determination ranges from 0 to 1 (closer to 1), and the higher the value, the better our model." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "r2: 0.93\n" ] } ], "source": [ "# Now let's compute another evaluation metric for our test dataset\n", "r2 = regEval.evaluate(predictionsAndLabelsDF, {regEval.metricName: \"r2\"})\n", "\n", "print(\"r2: {:.2f}\".format(r2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generally, assuming a Gaussian distribution of errors, a good model will have 68% of predictions within 1 RMSE and 95% within 2 RMSE of the actual value.\n", "\n", "Let's examine the predictions and see if a RMSE of 4.61 meets this criteria." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sqlContext.sql(\"DROP TABLE IF EXISTS Power_Plant_RMSE_Evaluation\")\n", "predictionsAndLabelsDF.selectExpr(\"PE\", \"Predicted_PE\", \"PE - Predicted_PE Residual_Error\", \\\n", " \"(PE - Predicted_PE) / {} Within_RSME\".format(rmse))\\\n", " .registerTempTable(\"Power_Plant_RMSE_Evaluation\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------+------------------+------------------+--------------------+\n", "| PE| Predicted_PE| Residual_Error| Within_RSME|\n", "+------+------------------+------------------+--------------------+\n", "|490.55| 492.8503868480957|-2.300386848095684|-0.49906688418980644|\n", "|489.86|483.93681202706864| 5.923187972931373| 1.2850303715518543|\n", "|489.11| 483.8504599224484|5.2595400775516055| 1.1410525498996038|\n", "|459.86| 487.4251507227025|-27.56515072270247| -5.980234974299983|\n", "|489.07|488.37401129434926|0.6959887056507341| 0.15099413172525689|\n", "+------+------------------+------------------+--------------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "sqlContext.sql(\"SELECT * from Power_Plant_RMSE_Evaluation\").show(5)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sqlContext.sql(\"SELECT Within_RSME from Power_Plant_RMSE_Evaluation\").toPandas().hist(bins=30)\n", "plt.ylabel('Density')\n", "plt.xlabel('With_RSME')\n", "plt.title('Histogram of Within_RSME');" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sql = \"SELECT case when Within_RSME <= 1.0 AND Within_RSME >= -1.0 then 1 \\\n", " when Within_RSME <= 2.0 AND Within_RSME >= -2.0 then 2 else 3 \\\n", " end RSME_Multiple, COUNT(*) AS Count \\\n", " FROM Power_Plant_RMSE_Evaluation \\\n", " GROUP BY case when Within_RSME <= 1.0 AND Within_RSME >= -1.0 then 1 \\\n", " when Within_RSME <= 2.0 AND Within_RSME >= -2.0 then 2 else 3 end\"\n", "RSME = sqlContext.sql(sql).toPandas()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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efj7eWLJE/3ztWqVWC4c+4rVkxZFw4RPGh3e58fpdO7N9Xq582hTBZSGi5wC4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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6,6))\n", "plt.pie(RSME.Count, labels=RSME.RSME_Multiple, autopct=\"%1.1f\", colors=['gold', 'yellowgreen', 'lightcoral']\n", " , startangle=140)\n", "plt.title(\"Pie of RSME_Multiple\")\n", "plt.legend(loc=\"best\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Right now, Linear model seems working pretty well. But we still need to go through Hyperparameter Turing to make sure we have the best model." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pyspark.ml.tuning import ParamGridBuilder, CrossValidator\n", "import numpy as np\n", "\n", "# Let's create our CrossValidator with 3 fold cross validation\n", "crossval = CrossValidator(estimator=lrPipeline, evaluator=regEval, numFolds=3)\n", "\n", "# We'll create a paramter grid using the ParamGridBuilder, and add the grid to the CrossValidator\n", "paramGrid = (ParamGridBuilder()\n", " .addGrid(lr.regParam, np.linspace(0.01, 0.1, 10))\n", " .build())\n", "crossval.setEstimatorParamMaps(paramGrid)\n", "\n", "# Now let's find and return the best model\n", "cvModel = crossval.fit(trainingSetDF).bestModel" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original Root Mean Squared Error: 4.61\n", "New Root Mean Squared Error: 4.60\n", "Old r2: 0.93\n", "New r2: 0.93\n" ] } ], "source": [ "# Now let's use cvModel to compute an evaluation metric for our test dataset: testSetDF\n", "predictionsAndLabelsDF = cvModel.transform(testSetDF).select(\"AT\", \"V\", \"AP\", \"RH\", \"PE\", \"Predicted_PE\")\n", "\n", "# Compute the RMSE evaluation metric for our test dataset\n", "rmseLR = regEval.evaluate(predictionsAndLabelsDF)\n", "\n", "# Compute the r2 evaluation metric for our test dataset\n", "r2LR = regEval.evaluate(predictionsAndLabelsDF, {regEval.metricName: \"r2\"})\n", "\n", "print(\"Original Root Mean Squared Error: {0:2.2f}\".format(rmse))\n", "print(\"New Root Mean Squared Error: {0:2.2f}\".format(rmseLR))\n", "print(\"Old r2: {0:2.2f}\".format(r2))\n", "print(\"New r2: {0:2.2f}\".format(r2LR))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at which regularization parameter is the best." ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Regularization parameter of the best model: 0.01\n" ] } ], "source": [ "print(\"Regularization parameter of the best model: {0:.2f}\".format(cvModel.stages[1]._java_obj.parent().getRegParam()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decision Tree Regressor" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Pipeline_43bb959a3a38628f3915" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyspark.ml.regression import DecisionTreeRegressor\n", "\n", "# Create a DecisionTreeRegressor\n", "dt = DecisionTreeRegressor()\n", "\n", "dt.setLabelCol(\"PE\")\\\n", " .setPredictionCol(\"Predicted_PE\")\\\n", " .setFeaturesCol(\"features\")\\\n", " .setSeed(100088121L)\\\n", " .setMaxBins(100)\n", "\n", "# Create a Pipeline\n", "dtPipeline = Pipeline()\n", "\n", "# Set the stages of the Pipeline\n", "dtPipeline.setStages([vectorizer, dt])" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [ "crossval.setEstimator(dtPipeline)\n", "\n", "# Let's tune over our dt.maxDepth parameter on the values 1 to 5\n", "paramGrid = (ParamGridBuilder().addGrid(dt.maxDepth, range(1, 6)).build())\n", "\n", "# Add the grid to the CrossValidator\n", "crossval.setEstimatorParamMaps(paramGrid)\n", "\n", "# Now let's find and return the best model\n", "dtModel = crossval.fit(trainingSetDF).bestModel" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LR Root Mean Squared Error: 4.60\n", "DT Root Mean Squared Error: 4.45\n", "LR r2: 0.93\n", "DT r2: 0.94\n" ] } ], "source": [ "predictionsAndLabelsDF = dtModel.transform(testSetDF).select(\"AT\", \"V\", \"AP\", \"RH\", \"PE\", \"Predicted_PE\")\n", "\n", "# Compute the RMSE evaluation metric for our test dataset\n", "rmseDT = regEval.evaluate(predictionsAndLabelsDF)\n", "\n", "# Compute the r2 evaluation metric for our test dataset\n", "r2DT = regEval.evaluate(predictionsAndLabelsDF, {regEval.metricName: \"r2\"})\n", "\n", "print(\"LR Root Mean Squared Error: {0:2.2f}\".format(rmseLR))\n", "print(\"DT Root Mean Squared Error: {0:2.2f}\".format(rmseDT))\n", "print(\"LR r2: {0:2.2f}\".format(r2LR))\n", "print(\"DT r2: {0:2.2f}\".format(r2DT))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We could see that Decision Tree Regressor is slightly better than Linear Regression." ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DecisionTreeRegressionModel (uid=DecisionTreeRegressor_43e492aeb5bd38e7bc4c) of depth 5 with 63 nodes\n" ] } ], "source": [ "print dtModel.stages[-1]._java_obj.toDebugString().split(\"\\n\")[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random Forest " ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Pipeline_4199908612cfa27e68e7" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyspark.ml.regression import RandomForestRegressor\n", "\n", "# Create a RandomForestRegressor\n", "rf = RandomForestRegressor()\n", "\n", "rf.setLabelCol(\"PE\")\\\n", " .setPredictionCol(\"Predicted_PE\")\\\n", " .setFeaturesCol(\"features\")\\\n", " .setSeed(100088121L)\\\n", " .setMaxDepth(8)\\\n", " .setNumTrees(30)\n", "\n", "# Create a Pipeline\n", "rfPipeline = Pipeline()\n", "\n", "# Set the stages of the Pipeline\n", "rfPipeline.setStages([vectorizer, rf])" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": true }, "outputs": [], "source": [ "crossval.setEstimator(rfPipeline)\n", "\n", "# Let's tune over our rf.maxBins parameter on the values 30 to 100 with step 20\n", "paramGrid = (ParamGridBuilder().addGrid(rf.maxBins, np.arange(30, 100, 20)).build())\n", "\n", "# Add the grid to the CrossValidator\n", "crossval.setEstimatorParamMaps(paramGrid)\n", "\n", "# Now let's find and return the best model\n", "rfModel = crossval.fit(trainingSetDF).bestModel" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LR Root Mean Squared Error: 4.60\n", "DT Root Mean Squared Error: 4.45\n", "RF Root Mean Squared Error: 3.91\n", "LR r2: 0.93\n", "DT r2: 0.94\n", "RF r2: 0.95\n" ] } ], "source": [ "predictionsAndLabelsDF = rfModel.transform(testSetDF).select(\"AT\", \"V\", \"AP\", \"RH\", \"PE\", \"Predicted_PE\")\n", "\n", "# Run the previously created RMSE evaluator, regEval, on the predictionsAndLabelsDF DataFrame\n", "rmseRF = regEval.evaluate(predictionsAndLabelsDF)\n", "\n", "# Now let's compute the r2 evaluation metric for our test dataset\n", "r2RF = regEval.evaluate(predictionsAndLabelsDF, {regEval.metricName: \"r2\"})\n", "\n", "print(\"LR Root Mean Squared Error: {0:.2f}\".format(rmseLR))\n", "print(\"DT Root Mean Squared Error: {0:.2f}\".format(rmseDT))\n", "print(\"RF Root Mean Squared Error: {0:.2f}\".format(rmseRF))\n", "print(\"LR r2: {0:.2f}\".format(r2LR))\n", "print(\"DT r2: {0:.2f}\".format(r2DT))\n", "print(\"RF r2: {0:.2f}\".format(r2RF))" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RandomForestRegressionModel (uid=rfr_f8074c3d43f1) with 30 trees\n" ] } ], "source": [ "print rfModel.stages[-1]._java_obj.toDebugString().split(\"\\n\")[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the end, we could see that Random Forest is the best model of the three. \n", "\n", "This notebook is an end-to-end tutorial of performing Extract-Transform-Load and Exploratory Data Analysis on a real-world dataset, and then applying several different machine learning algorithms to solve a supervised regression problem on the dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }