def Isotonic_Regression(filename, sc):
filename = "/Users/Jacob/SparkService/data/sample_isotonic_regression_data.txt"
data = sc.textFile(filename)
# Create label, feature, weight tuples from input data with weight set to default value 1.0.
parsedData = data.map(lambda line: tuple([float(x) for x in line.split(',')]) + (1.0,))
# Split data into training (60%) and test (40%) sets.
training, test = parsedData.randomSplit([0.6, 0.4], 11)
# Create isotonic regression model from training data.
# Isotonic parameter defaults to true so it is only shown for demonstration
model = IsotonicRegression.train(training)
# Create tuples of predicted and real labels.
predictionAndLabel = test.map(lambda p: (model.predict(p[1]), p[0]))
# Calculate mean squared error between predicted and real labels.
meanSquaredError = predictionAndLabel.map(lambda pl: math.pow((pl[0] - pl[1]), 2)).mean()
print("Mean Squared Error = " + str(meanSquaredError))
# Save and load model
#model.save(sc, "target/tmp/myIsotonicRegressionModel")
#sameModel = IsotonicRegressionModel.load(sc, "target/tmp/myIsotonicRegressionModel")
def main():
records = get_records()
records.cache()
mappings = [get_mapping(records, i) for i in range(2, 10)]
for m in mappings:
print m
cat_len = sum(map(len, mappings))
num_len = len(records.first()[11:15])
total_len = num_len + cat_len
#data = records.map(lambda r: LabeledPoint(extract_label(r), extract_features(r, cat_len, mappings)))
parsed_data = records.map(lambda r: (extract_label(
r), extract_sum_feature(r, cat_len, mappings), 1.0))
model = IsotonicRegression.train(parsed_data)
first = parsed_data.first()
print first
true_vs_predicted = parsed_data.map(lambda p: (p[1], model.predict(p[2])))
print "Isotonic Regression: " + str(true_vs_predicted.take(5))
calculate_print_metrics("Isotonic Regression", true_vs_predicted)
sc = SparkContext(appName="PythonIsotonicRegressionExample")
# $example on$
# Load and parse the data
def parsePoint(labeledData):
return (labeledData.label, labeledData.features[0], 1.0)
data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_isotonic_regression_libsvm_data.txt")
# Create label, feature, weight tuples from input data with weight set to default value 1.0.
parsedData = data.map(parsePoint)
# Split data into training (60%) and test (40%) sets.
training, test = parsedData.randomSplit([0.6, 0.4], 11)
# Create isotonic regression model from training data.
# Isotonic parameter defaults to true so it is only shown for demonstration
model = IsotonicRegression.train(training)
# Create tuples of predicted and real labels.
predictionAndLabel = test.map(lambda p: (model.predict(p[1]), p[0]))
# Calculate mean squared error between predicted and real labels.
meanSquaredError = predictionAndLabel.map(lambda pl: math.pow((pl[0] - pl[1]), 2)).mean()
print("Mean Squared Error = " + str(meanSquaredError))
# Save and load model
model.save(sc, "target/tmp/myIsotonicRegressionModel")
sameModel = IsotonicRegressionModel.load(sc, "target/tmp/myIsotonicRegressionModel")
# $example off$
# Load and parse the data
def parsePoint(labeledData):
return (labeledData.label, labeledData.features[0], 1.0)
data = MLUtils.loadLibSVMFile(
sc, "data/mllib/sample_isotonic_regression_libsvm_data.txt")
# Create label, feature, weight tuples from input data with weight set to default value 1.0.
parsedData = data.map(parsePoint)
# Split data into training (60%) and test (40%) sets.
training, test = parsedData.randomSplit([0.6, 0.4], 11)
# Create isotonic regression model from training data.
# Isotonic parameter defaults to true so it is only shown for demonstration
model = IsotonicRegression.train(training)
# Create tuples of predicted and real labels.
predictionAndLabel = test.map(lambda p: (model.predict(p[1]), p[0]))
# Calculate mean squared error between predicted and real labels.
meanSquaredError = predictionAndLabel.map(lambda pl: math.pow(
(pl[0] - pl[1]), 2)).mean()
print("Mean Squared Error = " + str(meanSquaredError))
# Save and load model
model.save(sc, "target/tmp/myIsotonicRegressionModel")
sameModel = IsotonicRegressionModel.load(
sc, "target/tmp/myIsotonicRegressionModel")
# $example off$