def evaluate(model, word_column="words", vectorizer="w2v"):
doc2vecs_df = featurize(word_column, vectorizer)
if type(model) == LinearSVC:
paramGrid = ParamGridBuilder() \
.addGrid(model.regParam, [0.1]) \
.build()
elif type(model) == GBTClassifier:
paramGrid = ParamGridBuilder() \
.addGrid(model.maxIter, [50]) \
.build()
elif type(model) == RandomForestClassifier:
paramGrid = ParamGridBuilder() \
.addGrid(model.maxBins, [100]) \
.build()
elif type(model) == MultilayerPerceptronClassifier:
paramGrid = ParamGridBuilder() \
.addGrid(model.layers, [[122, 50, 2]]) \
.build()
# .addGrid(model.layers, [[120, 2], [120, 50, 2], [120, 75, 50, 2]]) \
elif type(model) == FMClassifier:
paramGrid = ParamGridBuilder() \
.addGrid(model.stepSize, [.01, .001]) \
.build()
print('Evaluating...')
w2v_train_df, w2v_test_df = doc2vecs_df.randomSplit([0.8, 0.2])
si = StringIndexer(inputCol="LABEL", outputCol="label")
model_evaluator = MulticlassClassificationEvaluator(
labelCol="label", predictionCol="prediction", metricName="f1")
classifier_pipeline = Pipeline(stages=[si, model])
crossval = CrossValidator(estimator=classifier_pipeline,
estimatorParamMaps=paramGrid,
evaluator=model_evaluator,
numFolds=5)
fit_model = crossval.fit(doc2vecs_df)
predictions = fit_model.transform(w2v_test_df)
# predictions.toPandas().to_csv('predictions.csv')
# predictions.groupBy('prediction', 'label', 'PRODUCT_CATEGORY')
# predictions.describe()
summarizer = Summarizer.metrics("mean", "count")
predictions.select(
summarizer.summary(predictions.filter(
predictions.label == 1).pos)).show(truncate=False)
preds_and_labels = predictions.select(['prediction', 'label'])
metrics = MulticlassMetrics(preds_and_labels.rdd.map(tuple))
print('Confusion Matrix')
print(metrics.confusionMatrix().toArray())
# Overall statistics
precision = metrics.precision(1.0)
recall = metrics.recall(1.0)
f1Score = metrics.fMeasure(1.0)
print("Summary Stats")
print("Precision = %s" % precision)
print("Recall = %s" % recall)
print("F1 Score = %s" % f1Score)
accuracy = model_evaluator.evaluate(predictions)
trainingSummary = fit_model.bestModel.stages[-1].extractParamMap()
print(trainingSummary)
return accuracy
def task_6(data_io, product_processed_data):
# -----------------------------Column names--------------------------------
# Inputs:
category_column = 'category'
# Outputs:
categoryIndex_column = 'categoryIndex'
categoryOneHot_column = 'categoryOneHot'
categoryPCA_column = 'categoryPCA'
# -------------------------------------------------------------------------
# ---------------------- Your implementation begins------------------------
indexer = M.feature.StringIndexer(inputCol=category_column,
outputCol=categoryIndex_column,
handleInvalid="error")
indexed_model = indexer.fit(product_processed_data).transform(
product_processed_data)
encoder = M.feature.OneHotEncoderEstimator(
dropLast=False,
inputCols=[categoryIndex_column],
outputCols=[categoryOneHot_column])
encodded_model = encoder.fit(indexed_model).transform(indexed_model)
pca = M.feature.PCA(k=15,
inputCol=categoryOneHot_column,
outputCol=categoryPCA_column)
pca_model = pca.fit(encodded_model).transform(encodded_model)
summarizer = Summarizer.metrics("mean")
count_total = pca_model.count()
meanVector_categoryPCA = pca_model.select(
summarizer.summary(pca_model.categoryPCA)).head()[0][0]
meanVector_categoryOneHot = pca_model.select(
summarizer.summary(pca_model.categoryOneHot)).head()[0][0]
# -------------------------------------------------------------------------
# ---------------------- Put results in res dict --------------------------
res = {
'count_total': None,
'meanVector_categoryOneHot': [
None,
],
'meanVector_categoryPCA': [
None,
]
}
# Modify res:
res['count_total'] = count_total
res['meanVector_categoryOneHot'] = meanVector_categoryOneHot
res['meanVector_categoryPCA'] = meanVector_categoryPCA
# -------------------------------------------------------------------------
# ----------------------------- Do not change -----------------------------
data_io.save(res, 'task_6')
return res
def task_6(data_io, product_processed_data):
# -----------------------------Column names--------------------------------
# Inputs:
category_column = 'category'
# Outputs:
categoryIndex_column = 'categoryIndex'
categoryOneHot_column = 'categoryOneHot'
categoryPCA_column = 'categoryPCA'
# -------------------------------------------------------------------------
# ---------------------- Your implementation begins------------------------
step1 = product_processed_data[[category_column]]
stringIndexer = M.feature.StringIndexer(inputCol="category",
outputCol="indexed_category",
handleInvalid="error",
stringOrderType="frequencyDesc")
inputs = stringIndexer.getOutputCol()
OHencoder = M.feature.OneHotEncoderEstimator(inputCols=[inputs],
outputCols=['categoryOneHot'],
dropLast=False)
pca_ = M.feature.PCA(inputCol="categoryOneHot",
outputCol='categoryPCA',
k=15)
pipeline = Pipeline(stages=[stringIndexer, OHencoder, pca_])
pipelineFit = pipeline.fit(step1)
output = pipelineFit.transform(step1)
sum_mean = Summarizer.metrics("mean")
meanVector_categoryOneHot = output.select(
Summarizer.mean(output.categoryOneHot)).head()[0]
meanVector_categoryPCA = output.select(Summarizer.mean(
output.categoryPCA)).head()[0]
# -------------------------------------------------------------------------
# ---------------------- Put results in res dict --------------------------
res = {
'count_total': None,
'meanVector_categoryOneHot': [
None,
],
'meanVector_categoryPCA': [
None,
]
}
# Modify res:
res['count_total'] = output.count()
res['meanVector_categoryOneHot'] = meanVector_categoryOneHot
res['meanVector_categoryPCA'] = meanVector_categoryPCA
# -------------------------------------------------------------------------
# ----------------------------- Do not change -----------------------------
data_io.save(res, 'task_6')
return res
from pyspark.sql import SparkSession
from pyspark.ml.stat import Summarizer
from pyspark.sql import Row
from pyspark.ml.linalg import Vectors
spark = SparkSession.builder.getOrCreate()
spark.sparkContext.setLogLevel("ERROR")
sc = spark.sparkContext
df = sc.parallelize([
Row(weight=1.0, features=Vectors.dense(1.0, 1.0, 1.0)),
Row(weight=0.0, features=Vectors.dense(1.0, 2.0, 3.0))
]).toDF()
summarizer = Summarizer.metrics("mean", "count")
df.show()
df.select(summarizer.summary(df.features, df.weight)).show(truncate=False)
df.select(summarizer.summary(df.features)).show(truncate=False)
df.select(Summarizer.mean(df.features, df.weight)).show(truncate=False)
df.select(Summarizer.mean(df.features)).show(truncate=False)
spark.stop()
(Vectors.dense([4.0, 5.0, 0.0, 3.0])),
(Vectors.dense([6.0, 7.0, 0.0, 8.0])),
(Vectors.sparse(4, [(0, 9.0), (3, 1.0)]))])
rdd_data = more_data.map(lambda line: tuple([float(x) for x in line]))
summary = Statistics.colStats(rdd_data)
print("Mean:" + str(summary.mean())) # a dense vector containing the mean value for each column
print("Variance:" + str(summary.variance())) # column-wise variance
print("Non zeros:" + str(summary.numNonzeros()))
print("Count:" + str(summary.count()))
print("Min:" + str(summary.min()))
print("Max:" + str(summary.max()))
# Examples with Summarizer
summarizer = Summarizer.metrics("mean", "count", "min", "max", "variance")
# compute statistics for multiple metrics
data_frame.select(summarizer.summary(data_frame.features)).show(truncate=False)
# compute statistics for single metric "mean"
data_frame.select(Summarizer.mean(data_frame.features)).show(truncate=False)
spark_session.stop()
from pyspark.ml.linalg import Vectors
# $example off$
if __name__ == "__main__":
spark = SparkSession \
.builder \
.appName("SummarizerExample") \
.getOrCreate()
sc = spark.sparkContext
# $example on$
df = sc.parallelize([Row(weight=1.0, features=Vectors.dense(1.0, 1.0, 1.0)),
Row(weight=0.0, features=Vectors.dense(1.0, 2.0, 3.0))]).toDF()
# create summarizer for multiple metrics "mean" and "count"
summarizer = Summarizer.metrics("mean", "count")
# compute statistics for multiple metrics with weight
df.select(summarizer.summary(df.features, df.weight)).show(truncate=False)
# compute statistics for multiple metrics without weight
df.select(summarizer.summary(df.features)).show(truncate=False)
# compute statistics for single metric "mean" with weight
df.select(Summarizer.mean(df.features, df.weight)).show(truncate=False)
# compute statistics for single metric "mean" without weight
df.select(Summarizer.mean(df.features)).show(truncate=False)
# $example off$
spark.stop()
####### Using correlation and Summarizer
#Select features
features = ["age", "charges", "customer_contacts", "attrition"]
va = VectorAssembler(inputCols= features, outputCol = "features") #Create Vector Assember
featuresData = va.transform(rawData) #transform original dataset to include new col of vectors
featuresData.show(n=2)
#Calculate correlation and display
r1 = Correlation.corr(featuresData, "features", method = 'pearson').head()
print("Pearson correlation matrix:\n" + str(r1[0]))
#Calculate mean statistic for the list of features in order
summarizer = Summarizer.metrics("mean")
featuresData.select(summarizer.summary(featuresData.features)).show(truncate=False)
"""**Split the Spark Dataframe into Train and Test**"""
#Splitting dataframe with randomsplit
splits = rawData.randomSplit(weights= [.7, .3], seed= 12345)
print("training obs count: ", splits[0].count())
print("test obs count: ", splits[1].count())
train = splits[0]
test = splits[1]
"""**Feature Engineering & Define Model**"""
output.select("features").show(10, truncate=False)
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.stat import Summarizer
assembler = VectorAssembler(inputCols=[
'year', 'population', 'labor_force', 'population_percent',
'employed_total', 'employed_percent', 'agrictulture_ratio',
'nonagriculture_ratio', 'unemployed', 'unemployed_percent', 'not_in_labor'
],
outputCol="features")
assembled = assembler.transform(employment_df)
summarizer = Summarizer.metrics("max", "mean").summary(assembled["features"])
assembled.select(summarizer).show(truncate=False)
assembled.select(Summarizer.variance(assembled.features)).show(truncate=False)
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.stat import Correlation
assembler = VectorAssembler(inputCols=[
"date", "day", "period", "nswprice", "nswdemand", "vicprice", "vicdemand",
"transfer"
],
outputCol="features")
assembled = assembler.transform(electricity_df)