def grid_search(spark):
sample_ratio = 0.05
neg_samples = get_negative_samples(spark).sample(sample_ratio).na.fill(0)
pos_samples = get_positive_samples(spark).sample(sample_ratio).na.fill(0)
df = get_dataset_df(spark, pos_samples, neg_samples).na.fill(0)
trainDF, testDF = df.randomSplit([0.8, 0.2], seed=0)
xgboost = XGBoostEstimator(featuresCol="features",
labelCol="label",
predictionCol="prediction")
pipeline = Pipeline().setStages([xgboost])
model = pipeline.fit(trainDF)
paramGrid = (ParamGridBuilder().addGrid(
xgboost.max_depth, [x for x in range(3, 20, 6)]).addGrid(
xgboost.eta, [x for x in np.linspace(0.2, 0.6, 4)]).addGrid(
xgboost.scale_pos_weight,
[x for x in np.linspace(0.03, 1.0, 3)]).build())
evaluator = BinaryClassificationEvaluator(labelCol="label",
rawPredictionCol="probabilities",
metricName="areaUnderPR")
cv = (CrossValidator().setEstimator(pipeline).setEvaluator(
evaluator).setEstimatorParamMaps(paramGrid).setNumFolds(3))
cvModel = cv.fit(trainDF)
bestModel = (cvModel.bestModel.asInstanceOf[PipelineModel].stages(
2).asInstanceOf[XGBoostClassificationModel])
with open(workdir + 'data/xgboost_tuning_results_1.txt', 'w') as file:
for model, result in zip(model.subModels[0], model.avgMetrics):
file.write('==================================\n')
for stage in model.stages:
params = stage.extractParamMap()
for k in params:
file.write(f'{k.name}: {params[k]}\n')
file.write(f"Area under PR: {result}\n")
prediction = bestModel.transform(testDF)
prediction = prediction.withColumn("rawPrediction",
prediction['probabilities'])
area_under_PR, f1_score = evaluate_binary_classifier(prediction)
with open(workdir + 'data/xgboost_tuning_perf_1.txt', 'w') as file:
file.write(f"Area Under PR = {area_under_PR}\nF1 score = {f1_score}")
return
#!/usr/bin/env python
import accident_prediction_montreal
from pyspark.sql.functions import udf, min, max, col
from pyspark.sql.types import FloatType
from pyspark.ml.feature import VectorAssembler
from preprocess import get_negative_samples, get_positive_samples
from utils import init_spark
from preprocess import get_dataset_df
from export_results import *
result_dir = create_result_dir('base')
spark = init_spark()
neg_samples = get_negative_samples(spark).sample(0.5)
pos_samples = get_positive_samples(spark)
imbalance_ratio = (neg_samples.count() / pos_samples.count())
train_set, test_set = get_dataset_df(spark, pos_samples, neg_samples)
train_set, test_set = train_set.persist(), test_set.persist()
get_accidents_count = udf(lambda v: float(v[7]), FloatType())
def fit(train_set):
accidents_count = train_set.select(
get_accidents_count('features').alias('accidents_count'), 'label')
accidents_count_to_proba = []
for i in range(377):
accidents_count_higher = \
accidents_count.filter(col('accidents_count') >= i)
proba = (accidents_count_higher.filter(col('label') == 1.0).count() /
from random_forest import balanced_random_forest_tuning, \
compute_precision_recall, \
compute_precision_recall_graph
from preprocess import get_positive_samples, \
get_negative_samples, \
get_dataset_df
from evaluate import evaluate_binary_classifier
from utils import init_spark
from workdir import workdir
spark = init_spark()
i = 1
sampleFraction = 0.01
neg_samples = get_negative_samples(spark).sample(sampleFraction)
pos_samples = get_positive_samples(spark).sample(sampleFraction)
df = get_dataset_df(spark, pos_samples, neg_samples)
(train_set, test_set) = df.randomSplit([0.8, 0.2])
(train_set, test_set) = (train_set.persist(), test_set.persist())
model = balanced_random_forest_tuning(train_set)
with open(workdir + f'data/brf_tuning_results_{i}.txt', 'w') as file:
for model, result in zip(model.subModels[0], model.avgMetrics):
file.write('==================================\n')
for stage in model.stages:
params = stage.extractParamMap()
for k in params:
file.write(f'{k.name}: {params[k]}\n')
file.write(f"Area under PR: {result}\n")
#!/usr/bin/env python
import accident_prediction_montreal
from preprocess import get_negative_samples, get_positive_samples
from utils import init_spark
from workdir import workdir
spark = init_spark()
neg_samples = \
get_negative_samples(spark,
save_to='data/negative-sample-new.parquet',
sample_ratio=1e-3)
print(neg_samples.count())