def main():
records = get_records()
records.cache()
print "Mapping of first categorical feature column: %s" % get_mapping(
records, 2)
# extract all the catgorical mappings
mappings = [get_mapping(records, i) for i in range(2, 10)]
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)))
data_dt = records.map(
lambda r: LabeledPoint(extract_label(r), extract_features_dt(r)))
cat_features = dict([(i - 2, len(get_mapping(records, i)) + 1)
for i in range(2, 10)])
print "Categorical feature size mapping %s" % cat_features
# train the model again
dt_model = DecisionTree.trainRegressor(
data_dt, categoricalFeaturesInfo=cat_features)
preds = dt_model.predict(data_dt.map(lambda p: p.features))
actual = data.map(lambda p: p.label)
true_vs_predicted_dt = actual.zip(preds)
calculate_print_metrics("Decision Tree Categorical Features",
true_vs_predicted_dt)
def get_train_test_data():
records = get_records()
records.cache()
print "Mapping of first categorical feature column: %s" % get_mapping(records, 2)
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)))
data_with_idx = data.zipWithIndex().map(lambda (k, v): (v, k))
test = data_with_idx.sample(False, 0.2, 42)
train = data_with_idx.subtractByKey(test)
train_data = train.map(lambda (idx, p): p)
test_data = test.map(lambda (idx, p) : p)
train_size = train_data.count()
test_size = test_data.count()
num_data = data.count()
print "Training data size: %d" % train_size
print "Test data size: %d" % test_size
print "Total data size: %d " % num_data
print "Train + Test size : %d" % (train_size + test_size)
return train_data, test_data
def main():
records = get_records()
records.cache()
# extract all the catgorical mappings
mappings = [get_mapping(records, i) for i in range(2,10)]
cat_len = sum(map(len, mappings))
num_len = len(records.first()[11:15])
data = records.map(lambda r: LabeledPoint(extract_label(r), extract_features(r, cat_len, mappings)))
data_dt = records.map(lambda r: LabeledPoint(extract_label(r), extract_features_dt(r)))
dt_model = DecisionTree.trainRegressor(data_dt, {})
preds = dt_model.predict(data_dt.map(lambda p: p.features))
actual = data.map(lambda p: p.label)
true_vs_predicted_dt = actual.zip(preds)
data_dt_log = data_dt.map(lambda lp: LabeledPoint(np.log(lp.label), lp.features))
dt_model_log = DecisionTree.trainRegressor(data_dt_log, {})
preds_log = dt_model_log.predict(data_dt_log.map(lambda p: p.features))
actual_log = data_dt_log.map(lambda p: p.label)
true_vs_predicted_dt_log = actual_log.zip(preds_log).map(lambda (t, p): (np.exp(t), np.exp(p)))
calculate_print_metrics("Decision Tree Log", true_vs_predicted_dt_log)
def main():
records = get_records()
records.cache()
# extract all the catgorical mappings
mappings = [get_mapping(records, i) for i in range(2, 10)]
cat_len = sum(map(len, mappings))
num_len = len(records.first()[11:15])
total_len = num_len + cat_len
print "Feature vector length for categorical features: %d" % cat_len
print "Feature vector length for numerical features: %d" % num_len
print "Total feature vector length: %d" % total_len
data = records.map(lambda r: LabeledPoint(
extract_label(r), extract_features(r, cat_len, mappings)))
data_dt = records.map(
lambda r: LabeledPoint(extract_label(r), extract_features_dt(r)))
first_point_dt = data_dt.first()
print "Decision Tree feature vector: " + str(first_point_dt.features)
print "Decision Tree feature vector length: " + str(
len(first_point_dt.features))
dt_model = DecisionTree.trainRegressor(data_dt, {})
preds = dt_model.predict(data_dt.map(lambda p: p.features))
actual = data.map(lambda p: p.label)
true_vs_predicted_dt = actual.zip(preds)
print "Decision Tree predictions: " + str(true_vs_predicted_dt.take(5))
print "Decision Tree depth: " + str(dt_model.depth())
print "Decision Tree number of nodes: " + str(dt_model.numNodes())
calculate_print_metrics("Decision Tree", true_vs_predicted_dt)
def main():
records = get_records()
print records.first()
print records.count()
records.cache()
print "Mapping of first categorical feature column: %s" % get_mapping(
records, 2)
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
print "Feature vector length for categorical features: %d" % cat_len
print "Feature vector length for numerical features: %d" % num_len
print "Total feature vector length: %d" % total_len
data = records.map(lambda r: LabeledPoint(
extract_label(r), extract_features(r, cat_len, mappings)))
first_point = data.first()
print "Linear Model feature vector:\n" + str(first_point.features)
print "Linear Model feature vector length: " + str(
len(first_point.features))
#linear_model = LinearRegressionWithSGD.train(data, iterations=10, step=0.1, intercept=False)
linear_model = LinearRegressionWithSGD.train(data,
iterations=10,
step=0.025,
regParam=0.0,
regType=None,
intercept=False)
true_vs_predicted = data.map(lambda p:
(p.label, linear_model.predict(p.features)))
print "Linear Model predictions: " + str(true_vs_predicted.take(5))
calculate_print_metrics("Linear Regression", true_vs_predicted)
def main():
records = get_records()
mappings = [get_mapping(records, i) for i in range(2,10)]
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)))
data_log = data.map(lambda lp: LabeledPoint(np.log(lp.label), lp.features))
model_log = LinearRegressionWithSGD.train(data_log, iterations=10, step=0.1)
true_vs_predicted_log = data_log.map(lambda p: (np.exp(p.label), np.exp(model_log.predict(p.features))))
calculate_print_metrics("Linear Regression Log", true_vs_predicted_log)
def get_train_test_data():
records = get_records()
records.cache()
# extract all the catgorical mappings
mappings = [get_mapping(records, i) for i in range(2, 10)]
cat_len = sum(map(len, mappings))
num_len = len(records.first()[11:15])
data = records.map(lambda r: LabeledPoint(
extract_label(r), extract_features(r, cat_len, mappings)))
data_dt = records.map(
lambda r: LabeledPoint(extract_label(r), extract_features_dt(r)))
data_with_idx_dt = data_dt.zipWithIndex().map(lambda (k, v): (v, k))
test_dt = data_with_idx_dt.sample(False, 0.2, 42)
train_dt = data_with_idx_dt.subtractByKey(test_dt)
train_data_dt = train_dt.map(lambda (idx, p): p)
test_data_dt = test_dt.map(lambda (idx, p): p)
return train_data_dt, test_data_dt
def main():
records = get_records()
records.cache()
mappings = [get_mapping(records, i) for i in range(2, 10)]
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)))
rr_model = RidgeRegressionWithSGD.train(data,
iterations=10,
step=0.1,
intercept=False)
true_vs_predicted_rr = data.map(lambda p:
(p.label, rr_model.predict(p.features)))
print "Ridge Regression Model predictions: " + str(
true_vs_predicted_rr.take(5))
calculate_print_metrics("Ridge Regression", true_vs_predicted_rr)
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)
def main():
records = get_records()
first = records.first()
records.cache()
# extract all the catgorical mappings
mappings = [get_mapping(records, i) for i in range(2, 10)]
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)))
first_point = data.first()
gbt_model = GradientBoostedTrees.trainRegressor(data,
categoricalFeaturesInfo={},
numIterations=3)
true_vs_predicted_gbt = data.map(lambda p:
(p.label, gbt_model.predict(p.features)))
predictions = gbt_model.predict(data.map(lambda x: x.features))
labelsAndPredictions = data.map(lambda lp: lp.label).zip(predictions)
print "GradientBoosted Trees predictions: " + str(
labelsAndPredictions.take(5))
mse = labelsAndPredictions.map(lambda (v, p): (v - p) * (v - p)).sum() /\
float(data.count())
mae = labelsAndPredictions.map(lambda (v, p): np.abs(v - p)).sum() /\
float(data.count())
rmsle = labelsAndPredictions.map(lambda (v,p) : ((np.log(p + 1) - np.log(v + 1))**2)).sum() /\
float(data.count())
print('Gradient Boosted Trees - Mean Squared Error = ' + str(mse))
print('Gradient Boosted Trees - Mean Absolute Error = ' + str(mae))
print('Gradient Boosted Trees - Mean Root Mean Squared Log Error = ' +
str(rmsle))