{"cells":[{"cell_type":"markdown","source":["##![LearnAI Header](https://coursematerial.blob.core.windows.net/assets/LearnAI_header.png)"],"metadata":{}},{"cell_type":"markdown","source":["# Hyperparameter tuning using Random Forests\n\nIn this lab, we are going to use a [Random Forest](https://en.wikipedia.org/wiki/Random_forest) in combination with a parameter grid and cross-validation to perform hyperparameter tuning to improve our solution for Predictive Maintenance.\n\nAdditional Documentation:\n- Spark API documentation for [Random Forests](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#pyspark.ml.regression.RandomForestClassifier)\n- Spark ML Guide for [Random Forests](https://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-classifier)"],"metadata":{}},{"cell_type":"markdown","source":["## Read the data"],"metadata":{}},{"cell_type":"markdown","source":["Let's load and cache the data once more."],"metadata":{}},{"cell_type":"code","source":["df = spark.read.parquet(\"dbfs:/FileStore/tables/preprocessed\").cache()\ndf = df.withColumnRenamed(\"y_0\", \"label\") # this is the label we're going to use\n\ndisplay(df)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["
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322015-03-22T00:00:00.000+000015282.0793.01036.0571.0411.0258.02014.02014.0978.0258.0618.02014.0978.0103.879321181620387.0562698996761455459.060186488301856.4609593226101141.227382567328235.668634830464441171.2407305689485225.044911937768430000
322015-03-22T01:00:00.000+000015283.0794.01037.0572.0412.0259.02015.02015.0979.0259.0619.02015.0979.0101.927663340969895.813729359231398455.6181237113162356.9583886107934242.255628131728555.131612356411994177.086612232956517.2640144410050060000
322015-03-22T02:00:00.000+000015284.0795.01038.0573.0413.0260.02016.02016.0980.0260.0620.02016.0980.0103.24425944470397.781789225930997472.571807762072830.89945609597592543.492985764343373.1464827813147282172.188937364868767.856848275297060000
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322015-04-05T10:00:00.000+000015628.0195.01382.03.0757.0604.02360.02360.01324.0604.0964.02360.01324.0102.955009928982453.209090272140571506.749509785870570.1589683717221841.610107906665153.7170729264843563170.461693304486236.9006470916774060000
322015-04-05T11:00:00.000+000015629.0196.01383.04.0758.0605.02361.02361.01325.0605.0965.02361.01325.098.740072074936898.421290934369408493.642556307705858.4089151059959940.771442309824653.9918413194435605178.0742932207627417.5949776278407020000
322015-04-05T12:00:00.000+000015630.0197.01384.05.0759.0606.02362.02362.01326.0606.0966.02362.01326.099.48505793845099.444891036317408509.157797972964542.9932293771912238.465591071988432.019227099788039180.7344009430432518.463305592895530000
322015-04-05T13:00:00.000+000015631.0198.01385.06.0760.0607.02363.02363.01327.0607.0967.02363.01327.0101.6138576042711910.29030304414476475.1584281936552539.1721464730374838.208877404898421.5526998343654226187.5764445408107713.502902751077940000
322015-04-05T14:00:00.000+000015632.0199.01386.07.0761.0608.02364.02364.01328.0608.0968.02364.01328.0105.898199968122213.502548397361313469.7762547887402343.0262415599361238.218754966978651.5431867479781718184.8996987217335318.4161681451886250000
322015-04-05T15:00:00.000+000015633.0200.01387.08.0762.0609.02365.02365.01329.0609.0969.02365.01329.0111.696940189463254.996833774513497454.218892338296729.185426909690339.682156296497653.5077022950782535167.9344660245457426.4350653354873550000
322015-04-05T16:00:00.000+000015634.0201.01388.09.0763.0610.02366.02366.01330.0610.0970.02366.01330.0109.394306573748757.601029147174564442.8073581320655311.93177268164257639.244129537051734.034858943479992162.7342419940222322.648196557989170000
322015-04-05T17:00:00.000+000015635.0202.01389.010.0764.0611.02367.02367.01331.0611.0971.02367.01331.0110.046620219771757.524004277715391460.1749535245014625.40911969043838838.94135046710673.9784602987326383157.4202103433052615.8613339345461740000
322015-04-05T18:00:00.000+000015636.0203.01390.011.0765.0612.02368.02368.01332.0612.0972.02368.01332.0103.83711829386827.89611664843841493.65168409872357.39620060834976638.247896591858334.5965344535872354154.9228093224642516.327362117800720000
322015-04-05T19:00:00.000+000015637.0204.01391.012.0766.0613.02369.02369.01333.0613.0973.02369.01333.0103.272319259306947.349300739667003495.3827290113912355.9264769392043737.6463182031649743.5601229828876244166.7817615856747613.580693796886020000
322015-04-05T20:00:00.000+000015638.0205.01392.013.0767.0614.02370.02370.01334.0614.0974.02370.01334.0102.388834573325977.964688646442502475.8204247879250584.1306851002626839.137478924602123.4313081552395794171.8442537507702617.5486085777895830000
322015-04-05T21:00:00.000+000015639.0206.01393.014.0768.0615.02371.02371.01335.0615.0975.02371.01335.0101.255448401555236.670592053947905451.6920644697129389.6547898247440138.864740958505223.4838601777209255174.2459909835917.4848172413667240000
322015-04-05T22:00:00.000+000015640.0207.01394.015.0769.0616.02372.02372.01336.0616.0976.02372.01336.0103.821046968022274.732184933006682426.722259415556549.18088859350941539.8591554111933562.0244708184819524177.7790395520962410.8203802405857040000
322015-04-05T23:00:00.000+000015641.0208.01395.016.0770.0617.02373.02373.01337.0617.0977.02373.01337.0102.737546941619773.889767817232084422.3757102290657645.6794092174772836.296985927588575.8642272069287715173.635990930352511.8071848744802460000
322015-04-06T00:00:00.000+000015642.0209.01396.017.0771.0618.02374.02374.01338.0618.0978.02374.01338.0103.103125245906943.1833022993201423452.6135453573159639.7667974761521934.0285192282139955.062564160450547173.290429221216511.2298470582808820000
322015-04-06T01:00:00.000+000015643.0210.01397.018.0772.0619.02375.02375.01339.0619.0979.02375.01339.0101.808422041680962.729845779459619468.411499006151519.68761607962743634.569834146202675.698328184445998168.70403598519212.6003574774655930000
322015-04-06T02:00:00.000+000015644.0211.01398.019.0773.0620.02376.02376.01340.0620.0980.02376.01340.0105.145533608033458.68356823912892466.6433285790959619.37449310355979634.672851680920735.7916683012270695168.9394368117900212.4677769708231450000
322015-04-06T03:00:00.000+000015645.0212.01399.020.0774.0621.02377.02377.01341.0621.0981.02377.01341.0104.919572291684468.747807328608319460.336901518414530.1103747067898437.95445562454084.020995116522143172.5261022057994813.1549526968784780000
322015-04-06T04:00:00.000+000015646.0213.01400.021.0775.0622.02378.02378.01342.0622.0982.02378.01342.0104.90320965624558.763799773917269467.80103619515341.8789521583405240.693646427706351.7961811737464364160.31070857870317.0807275138712350000
322015-04-06T05:00:00.000+000015647.0214.01401.022.0776.0623.02379.02379.01343.0623.0983.02379.01343.097.7599318772257819.027913554694774454.071815697381851.5301610267837339.783505772637032.815842616565917162.45492194808717.4220662489973180000
322015-04-06T06:00:00.000+000015648.0215.01402.023.0777.0624.02380.02380.01344.0624.0984.02380.01344.094.8149058105170215.474888207570832459.902409641032554.643396499813140.7136721120084962.9631117411381815163.8326323109002417.798594895958330000
322015-04-06T07:00:00.000+000015649.0216.01403.024.0778.0625.02381.02381.01345.0625.0985.02381.01345.094.5541859416937715.305111590280386467.1504205459972449.1020656870065939.21587007025394.1284341283999915164.3945447736132618.4905273665261980000
322015-04-06T08:00:00.000+000015650.0217.01404.025.0779.0626.02382.02382.01346.0626.0986.02382.01346.097.4590425740205117.2701394450736464.89668487857145.87289357062406538.709242822668573.5940071073666826173.3653857628726.3725082275376180000
322015-04-06T09:00:00.000+000015651.0218.01405.026.0780.0627.02383.02383.01347.0627.0987.02383.01347.0105.56089511208053.5052275198402816468.34172141850340.4591841254066737.8589454314295254.567506423955181170.2970757737932411.1520814013428250000
322015-04-06T10:00:00.000+000015652.0219.01406.027.0781.0628.02384.02384.01348.0628.0988.02384.01348.0104.60957741630153.8437937831937967476.229763242537548.3679818220419936.431618057609023.3954293125915544165.984689156693514.4159513973367770000
322015-04-06T11:00:00.000+000015653.0220.01407.028.0782.0629.02385.02385.01349.0629.0989.02385.01349.0105.655721150786763.313313580520711479.335172970131546.33109702581961636.6475676867265043.2914370505354604162.9735723655252610.7486062460998880000
322015-04-06T12:00:00.000+000015654.0221.01408.029.0783.0630.02386.02386.01350.0630.0990.02386.01350.0101.548557553640535.47710509651552456.949908732254846.56668640781436637.0033301405366763.933559309752731160.843185816050248.3519642278083080000
"]}}],"execution_count":5},{"cell_type":"code","source":["keys = ['machineID', 'datetime']\nX_keep = ['diff_maint_1', 'diff_error_1', 'volt_sd_3', 'diff_fail_3', 'pressure_ma_3', 'pressure_sd_3', 'diff_fail_1', 'diff_fail_0', 'age', 'vibration_ma_3', 'rotate_ma_3', 'diff_error_2', 'diff_fail_2', 'diff_error_3', 'diff_maint_2', 'volt_ma_3', 'diff_maint_0', 'vibration_sd_3', 'diff_maint_3', 'rotate_sd_3', 'diff_error_0', 'diff_error_4']\nY_keep = ['y_0', 'y_1', 'y_2', 'y_3']"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":6},{"cell_type":"code","source":["from pandas import datetime\nfrom pyspark.sql.functions import col, hour\n\n# we sample every nth row of the data using the `hour` function\ndf_train = df.filter((col('datetime') < datetime(2015, 10, 1))) # & (hour(col('datetime')) % 3 == 0))\ndf_test = df.filter(col('datetime') > datetime(2015, 10, 15))"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":7},{"cell_type":"code","source":["display(df_train.describe())"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["
summarymachineIDagediff_error_0diff_error_1diff_error_2diff_error_3diff_error_4diff_fail_0diff_fail_1diff_fail_2diff_fail_3diff_maint_0diff_maint_1diff_maint_2diff_maint_3pressure_ma_3pressure_sd_3rotate_ma_3rotate_sd_3vibration_ma_3vibration_sd_3volt_ma_3volt_sd_3labely_1y_2y_3
count654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600
mean50.511.33767.0613458600673998.2097525206233933.5928353192791020.79924075771461615.12518942865882133.1543996333641935.93174610449142652.42126947754332349.70948212648951113.88324472960581109.7713840513291144.65145737855191120.9993217231897100.825134520184389.23999324497866446.671080080802446.20725438107571440.3964837222775944.619880429306799170.7915328226606813.815335825063860.0146379468377635190.018849679193400550.0108203483043079750.015073327222731438
stddev28.8660920963801575.8276197440095325763.1257842489293955.9490918887524994.1383169517087995.11941541641161427.18205098762971655.12930839179171552.7992166091151935.5717588200811866.57333153854941000.67492911812771023.52386226274111038.59935908860621038.3281999294046.783110559195193.91649947352247429.65933821520036819.4865553158303163.17229818138536231.95330447988257478.4480169118941655.8454271175128820.120098706839455670.135994106639518970.103456680392130280.1218447565916889
min100.00.00.00.00.00.00.00.00.00.00.00.00.076.00533246775320.09369705911616336198.7703373777620.2159233438022444622.97328948186140.016413819956125182135.97945655893550.02999196817971350000
max100205482.06645.06645.06617.06645.06645.06645.06645.06645.06645.06645.06645.06645.0164.8753240447777632.69926439338247576.923563024815164.9177646109864467.3120852001053715.995336982084046233.1357763038757654.5842292504167261111
"]}}],"execution_count":8},{"cell_type":"markdown","source":["## Define your Random Forest\n\nRandom forests and ensembles of decision trees are more powerful than a single decision tree alone. \n\nLet's take a look at all the hyperparameters we could change in [RandomForestClassifier](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#pyspark.ml.classification.RandomForestClassifier).\n\nOr run the next cell to get an explanation of the hyperparameters for the Random Forest algorithm."],"metadata":{}},{"cell_type":"code","source":["from pyspark.ml.classification import RandomForestClassifier\n\nRandomForestClassifier().explainParams()"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["\n
Out[5]: "cacheNodeIds: If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. Users can set how often should the cache be checkpointed or disable it by setting checkpointInterval. (default: False)\\ncheckpointInterval: set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext. (default: 10)\\nfeatureSubsetStrategy: The number of features to consider for splits at each tree node. Supported options: 'auto' (choose automatically for task: If numTrees == 1, set to 'all'. If numTrees > 1 (forest), set to 'sqrt' for classification and to 'onethird' for regression), 'all' (use all features), 'onethird' (use 1/3 of the features), 'sqrt' (use sqrt(number of features)), 'log2' (use log2(number of features)), 'n' (when n is in the range (0, 1.0], use n * number of features. When n is in the range (1, number of features), use n features). default = 'auto' (default: auto)\\nfeaturesCol: features column name. (default: features)\\nimpurity: Criterion used for information gain calculation (case-insensitive). Supported options: entropy, gini (default: gini)\\nlabelCol: label column name. (default: label)\\nmaxBins: Max number of bins for discretizing continuous features. Must be >=2 and >= number of categories for any categorical feature. (default: 32)\\nmaxDepth: Maximum depth of the tree. (>= 0) E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. (default: 5)\\nmaxMemoryInMB: Maximum memory in MB allocated to histogram aggregation. If too small, then 1 node will be split per iteration, and its aggregates may exceed this size. (default: 256)\\nminInfoGain: Minimum information gain for a split to be considered at a tree node. (default: 0.0)\\nminInstancesPerNode: Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Should be >= 1. (default: 1)\\nnumTrees: Number of trees to train (>= 1). (default: 20)\\npredictionCol: prediction column name. (default: prediction)\\nprobabilityCol: Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities. (default: probability)\\nrawPredictionCol: raw prediction (a.k.a. confidence) column name. (default: rawPrediction)\\nseed: random seed. (default: -184448601186841989)\\nsubsamplingRate: Fraction of the training data used for learning each decision tree, in range (0, 1]. (default: 1.0)"\n
"]}}],"execution_count":10},{"cell_type":"markdown","source":["## Let's define a Pipeline\n\nNow that we have all of the feature transformations and estimators set up, let's put all of the stages together in the pipline."],"metadata":{}},{"cell_type":"code","source":["from pyspark.ml.feature import StandardScaler, VectorAssembler\nfrom pyspark.ml.classification import RandomForestClassifier\nfrom pyspark.ml import Pipeline\n\nvassembler = VectorAssembler(inputCols = X_keep, outputCol = \"features\")\n\nrf = (RandomForestClassifier()\n .setLabelCol(\"label\")\n .setFeaturesCol(\"features\")\n .setSeed(27))\n\npipeline = Pipeline(stages = [vassembler, rf])"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":12},{"cell_type":"markdown","source":["### Hands-on lab\nTry changing the values of `numTrees` and `maxDepth` to any values you like\n\nHINT: Take a look at the docs"],"metadata":{}},{"cell_type":"code","source":["#todo"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":14},{"cell_type":"code","source":["# maximize this cell to see the solution:\n \nrf.setNumTrees(100)\nrf.setMaxDepth(10)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["\n
Out[8]: RandomForestClassifier_befa1fa0433e\n
"]}}],"execution_count":15},{"cell_type":"markdown","source":["### End of lab"],"metadata":{}},{"cell_type":"code","source":["pipeline.getStages()"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["\n
Out[9]: [VectorAssembler_f1630a46be5c, RandomForestClassifier_befa1fa0433e]\n
"]}}],"execution_count":17},{"cell_type":"markdown","source":["If you want to look at what parameter each stage in the pipeline takes."],"metadata":{}},{"cell_type":"code","source":["pipeline.getStages()[0].extractParamMap()"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["\n
Out[10]: \n{Param(parent='VectorAssembler_f1630a46be5c', name='handleInvalid', doc="How to handle invalid data (NULL and NaN values). Options are 'skip' (filter out rows with invalid data), 'error' (throw an error), or 'keep' (return relevant number of NaN in the output). Column lengths are taken from the size of ML Attribute Group, which can be set using `VectorSizeHint` in a pipeline before `VectorAssembler`. Column lengths can also be inferred from first rows of the data since it is safe to do so but only in case of 'error' or 'skip')."): 'error',\n Param(parent='VectorAssembler_f1630a46be5c', name='inputCols', doc='input column names.'): ['diff_maint_1',\n 'diff_error_1',\n 'volt_sd_3',\n 'diff_fail_3',\n 'pressure_ma_3',\n 'pressure_sd_3',\n 'diff_fail_1',\n 'diff_fail_0',\n 'age',\n 'vibration_ma_3',\n 'rotate_ma_3',\n 'diff_error_2',\n 'diff_fail_2',\n 'diff_error_3',\n 'diff_maint_2',\n 'volt_ma_3',\n 'diff_maint_0',\n 'vibration_sd_3',\n 'diff_maint_3',\n 'rotate_sd_3',\n 'diff_error_0',\n 'diff_error_4'],\n Param(parent='VectorAssembler_f1630a46be5c', name='outputCol', doc='output column name.'): 'features'}\n
"]}}],"execution_count":19},{"cell_type":"markdown","source":["## Setup the grid for searching hyperparameters\n\nThere are a lot of hyperparamaters we could tune, and it would take a long time to manually configure.\n\nInstead of a manual (ad-hoc) approach, let's use Spark's [ParamGridBuilder](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#pyspark.ml.tuning.ParamGridBuilder) to find the optimal hyperparameters in a more systematic approach.\n\nIn this example notebook, we keep these trees shallow and use a relatively small number of trees. Let's define a grid of hyperparameters to test:\n - maxDepth: max depth of each decision tree in the RF ensemble (Use the values `2, 5, 10`)\n - numTrees: number of trees in each RF ensemble (Use the values `10, 50`)"],"metadata":{}},{"cell_type":"code","source":["from pyspark.ml.tuning import ParamGridBuilder\n\nparamGrid = (ParamGridBuilder()\n# .addGrid()\n# .addGrid()\n .build())"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":21},{"cell_type":"code","source":["# maximize this cell to see the solution:\n\nfrom pyspark.ml.tuning import ParamGridBuilder\n\nparamGrid = (ParamGridBuilder()\n .addGrid(rf.maxDepth, [3, 5, 10]) \n .addGrid(rf.numTrees, [5, 10, 20]) \n .build())"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":22},{"cell_type":"markdown","source":["## Define cross-validation procedure\n\nAs in the previous lab for *sentiment analysis*, we are going to use 3-fold cross validation."],"metadata":{}},{"cell_type":"markdown","source":["We pass in the `estimator` (pipeline), `evaluator`, and `estimatorParamMaps` to [CrossValidator](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#pyspark.ml.tuning.CrossValidator) so that it knows:\n- Which model to use\n- How to evaluate the model\n- What hyperparamters to set for the model\n\nWe can also set the number of folds we want to split our data into (3), as well as setting a seed so we all have the same split in the data."],"metadata":{}},{"cell_type":"code","source":["from pyspark.ml.evaluation import BinaryClassificationEvaluator\n\nfrom pyspark.ml.tuning import CrossValidator\n\nevaluator = (BinaryClassificationEvaluator()\n .setLabelCol(\"label\")\n .setRawPredictionCol(\"rawPrediction\")\n .setMetricName(\"areaUnderROC\"))\n\ncv = (CrossValidator()\n .setEstimator(pipeline) # our pipeline for building a random forest model\n .setEvaluator(evaluator) # the evaluator let's CV know how to evaluate models\n .setEstimatorParamMaps(paramGrid) # the hyper-pramaters we're iterating over\n .setNumFolds(3) # every hyper-parameter combination is evaluated using cross-validaton with 3-folds \n .setSeed(27)) # we set the seed for reproducibilty"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":25},{"cell_type":"code","source":["cv_rf_model = cv.fit(df_train)"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":26},{"cell_type":"markdown","source":["Let's take a look at the model with the best hyperparameter configuration"],"metadata":{}},{"cell_type":"code","source":["results = list(zip(cv_rf_model.getEstimatorParamMaps(), cv_rf_model.avgMetrics))\n\nfor x in results:\n print(x[0].values(), x[1])"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["\n
dict_values([3, 5]) 0.6052002822326324\ndict_values([5, 5]) 0.9040217073464585\ndict_values([10, 5]) 0.9748806472070224\ndict_values([3, 10]) 0.8088097454194326\ndict_values([5, 10]) 0.9277400376078913\ndict_values([10, 10]) 0.9765763939422862\ndict_values([3, 20]) 0.8543349570696941\ndict_values([5, 20]) 0.930211531735222\ndict_values([10, 20]) 0.9815567173806785\n
"]}}],"execution_count":28},{"cell_type":"markdown","source":["## Evaluate the result of your cross-validated hyperparameter tuning\n\nLet's see how well we did on the test set."],"metadata":{}},{"cell_type":"code","source":["df_pred = cv_rf_model.bestModel.transform(df_test)\ndisplay(df_pred.groupBy(\"label\", \"prediction\").count())"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["
labelpredictioncount
10.01518
00.0185641
11.0570
01.071
"]}}],"execution_count":30},{"cell_type":"code","source":["evaluator = BinaryClassificationEvaluator()\nevaluator.setLabelCol(\"label\")\nevaluator.setRawPredictionCol(\"rawPrediction\")\n\nauroc = evaluator.setMetricName(\"areaUnderROC\").evaluate(df_pred)\nprint(\"AUROC: {}\".format(auroc))"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["\n
AUROC: 0.9453818032770718\n
"]}}],"execution_count":31},{"cell_type":"markdown","source":["Once again let's save our pipeline in case we need to make future use of it."],"metadata":{}},{"cell_type":"code","source":["# cv_rf_model.write().overwrite().save(\"/dbfs/tmp/cv_rf_model_ipeline\")"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":33},{"cell_type":"markdown","source":["Interesting. The results are quite a bit lower for the test set than they were during cross-validation. Can you say why?"],"metadata":{}},{"cell_type":"code","source":["# You can ignore this code, we use it for testing our notebooks.\nassert auroc > .9"],"metadata":{},"outputs":[{"metadata":{},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":35},{"cell_type":"markdown","source":["#### Improving our model\n\nYou are not done yet! There are several ways we could further improve our model:\n* **Expert knowledge** \n* **Better tuning** \n* **Feature engineering**\n\nAs an exercise: Replace the Random Forest code with a Gradient Boosted tree, and vary the number of trees and depth of the trees. What do you find?\n\n*Good luck!*"],"metadata":{}}],"metadata":{"name":"02_random_forests","notebookId":4057188818416634},"nbformat":4,"nbformat_minor":0}