def get_negative_samples(spark,
use_cache=True,
save_to=None,
road_limit=None,
year_limit=None,
year_ratio=None,
weather_df=None,
sample_ratio=None,
accident_df=None):
"""
Note to self: 539 293 road, 43 848 generated dates,
nb dates for 1 year : 8760
year_limit: int or tuple of int
"""
cache_path = workdir + 'data/negative-samples.parquet'
if isdir(cache_path) and use_cache and save_to is None:
return spark.read.parquet(cache_path)
if save_to is not None:
cache_path = workdir + save_to
if isdir(cache_path):
raise ValueError(f"Directory {save_to} already exists")
road_df = get_road_df(spark, use_cache)
road_features_df = \
get_road_features_df(spark, road_df=road_df, use_cache=use_cache)
road_df = road_features_df.select('street_id')
dates_df = generate_dates_df(spark, year_limit, year_ratio)
if road_limit is not None:
road_df = road_df.limit(road_limit)
negative_samples = (dates_df.crossJoin(road_df))
if sample_ratio is not None:
negative_samples = negative_samples.sample(sample_ratio)
negative_samples = \
negative_samples.withColumn('sample_id',
monotonically_increasing_id())
accident_df = preprocess_accidents(accident_df or get_accident_df(spark))
if year_limit is not None:
accident_df = accident_df.filter(year('date').isin(year_limit))
weather_df = weather_df or get_weather_df(spark, accident_df)
negative_samples = negative_samples.join(road_features_df, 'street_id')
negative_sample_weather = \
get_weather_information(negative_samples, weather_df)
negative_samples = \
negative_samples.join(negative_sample_weather, 'sample_id')
negative_samples = add_date_features(negative_samples)
negative_samples = add_solar_features(negative_samples)
negative_samples = negative_samples.persist()
if use_cache:
negative_samples.write.parquet(cache_path)
return negative_samples
def get_weather():
if request.method == 'POST':
text = request.form
zipcode = text['zipcode']
df = get_weather_df(weather_creds['username'], weather_creds['password'],
str(weather_creds['port']), weather_creds['url'], zipcode)
plotbytes = get_weather_plots(df)
weatherdat = base64.encodebytes(plotbytes.read()).decode('ascii')
else:
weatherdat = None
zipcode = None
return render_template('weather.html', result=weatherdat, zipcode=zipcode)
def get_positive_samples(spark,
road_df=None,
weather_df=None,
year_limit=None,
use_cache=True,
limit=None):
if isinstance(year_limit, int):
year_limit = [year_limit]
elif isinstance(year_limit, tuple):
year_limit = list(year_limit)
elif not ((year_limit is None) or isinstance(year_limit, list)):
raise ValueError('Type of year_limit not authorized.')
cache_path = workdir + 'data/positive-samples.parquet'
if isdir(cache_path) and use_cache:
return spark.read.parquet(cache_path)
road_df = road_df or get_road_df(spark, use_cache)
accident_df = get_accident_df(spark, use_cache)
accident_df = preprocess_accidents(accident_df)
if year_limit is not None:
accident_df = accident_df.filter(year('date').isin(year_limit))
if limit is not None:
accident_df = accident_df.limit(limit)
weather_df = weather_df or get_weather_df(spark, accident_df)
road_features_df = \
(get_road_features_df(spark, road_df=road_df, use_cache=use_cache)
.drop('loc_lat', 'loc_long'))
match_acc_road = match_accidents_with_roads(spark, road_df, accident_df)
print(match_acc_road.head(10))
accident_df = accident_df.withColumnRenamed('accident_id', 'sample_id')
accident_weather = get_weather_information(accident_df, weather_df)
positive_samples = (accident_df.join(
accident_weather, 'sample_id').withColumnRenamed(
'sample_id',
'accident_id').join(match_acc_road, 'accident_id').join(
road_features_df,
'street_id').withColumnRenamed('accident_id', 'sample_id'))
positive_samples = add_date_features(positive_samples)
positive_samples = add_solar_features(positive_samples)
# positive_samples = positive_samples.persist(pyspark.StorageLevel.DISK_ONLY)
if use_cache:
positive_samples.write.parquet(cache_path)
return positive_samples
def get_positive_samples(spark,
road_df=None,
weather_df=None,
year_limit=None,
use_cache=True,
limit=None):
if isinstance(year_limit, int):
year_limit = [year_limit]
elif isinstance(year_limit, tuple):
year_limit = list(year_limit)
elif not ((year_limit is None) or isinstance(year_limit, list)):
raise ValueError("Type of year_limit not authorized.")
cache_path = workdir + "data/positive-samples.parquet"
if isdir(cache_path) and use_cache:
return spark.read.parquet(cache_path)
road_df = road_df or get_road_df(spark, use_cache)
accident_df = get_accident_df(spark, use_cache)
accident_df = preprocess_accidents(accident_df)
if year_limit is not None:
accident_df = accident_df.filter(year("date").isin(year_limit))
if limit is not None:
accident_df = accident_df.limit(limit)
weather_df = weather_df or get_weather_df(spark, accident_df)
road_features_df = get_road_features_df(spark,
road_df=road_df,
use_cache=use_cache).drop(
"loc_lat", "loc_long")
match_acc_road = match_accidents_with_roads(spark, road_df, accident_df)
accident_df = accident_df.withColumnRenamed("accident_id", "sample_id")
accident_weather = get_weather_information(accident_df, weather_df)
positive_samples = (accident_df.join(
accident_weather, "sample_id").withColumnRenamed(
"sample_id",
"accident_id").join(match_acc_road, "accident_id").join(
road_features_df,
"street_id").withColumnRenamed("accident_id", "sample_id"))
positive_samples = add_date_features(positive_samples)
positive_samples = add_solar_features(positive_samples)
positive_samples = positive_samples.persist()
if use_cache:
positive_samples.write.parquet(cache_path)
return positive_samples
#!/usr/bin/env python import accident_prediction_montreal from accidents_montreal import get_accident_df from weather import get_weather_df from utils import init_spark from preprocess import preprocess_accidents spark = init_spark() accident_df = preprocess_accidents(get_accident_df(spark)) df = get_weather_df(spark, accident_df)
