本文整理匯總了Python中Data.load_batch_data方法的典型用法代碼示例。如果您正苦於以下問題：Python Data.load_batch_data方法的具體用法？Python Data.load_batch_data怎麽用？Python Data.load_batch_data使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類Data的用法示例。
在下文中一共展示了Data.load_batch_data方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: main
# 需要導入模塊: import Data [as 別名]
# 或者: from Data import load_batch_data [as 別名]
def main(args=None):
# Setting up configuration TODO: use docopt
if(args):
configuration_string = args[0]
else:
configuration_string = "Configuration/default.conf"
config = ConfigParser.ConfigParser()
config.read(configuration_string)
rawdata_directory = config.get("Directories", "dir_rawdata")
storedmodel_directory = config.get("Directories", "dir_storedmodel")
cluster_json_directory = config.get("Directories", "dir_clusters")
# Parameters TODO: set these in config file
# Clustering
cluster_feature_names = ["StartLat", "StartLong", "EndLat", "EndLong"] # Which features to cluster over
clustering_alg = "KMeans" # Which Clustering Algorithm to use
cluster_model_file = config.get("Batch", "cluster_class_file")
cluster_params = {"max_clusters": 10, "n_init": 10} # Parameters for KMeans
# Initial Classification
init_class_alg = "RandomForest"
init_class_model_file = config.get("Batch", "init_class_file")
init_class_feature_names = ["StartLat", "StartLong"] # Which features to cluster over
# Online Classification
online_class_alg = "RandomForest"
online_class_model_file = config.get("Batch", "online_class_file")
online_class_feature_names = ["Latitude", "Longitude", "StartLat", "StartLong"]
# Read in batch data
raw_data = sc.textFile(rawdata_directory)
json_data = raw_data.map(lambda x: json.loads(x))
pair_rdd = json_data.filter(bool).map(lambda x: (x["journey_id"], x))
raw_journeys = pair_rdd.combineByKey(lambda value: [value], lambda acc, value: acc + [value], lambda acc1, acc2: add(acc1, acc2))
processed_journeys = raw_journeys.mapValues(lambda x: Data.load_batch_data(x))
journeys = processed_journeys.map(lambda x: (x[1].data["vin"][0], x[1]))
journeys_by_vin = journeys.combineByKey(lambda value: [value], lambda acc, value: acc + [value], lambda acc1, acc2: add(acc1, acc2))
# Build, assign and save clusters
journeys_with_id = journeys_by_vin.mapValues(lambda data: Models.cluster(clustering_alg, cluster_feature_names, cluster_params, data))
journeys_with_id.persist()
journey_clusters = journeys_with_id.mapValues(lambda journeys: Data.create_journey_clusters(journeys)).persist()
journey_clusters_local = journey_clusters.collectAsMap()
joblib.dump(journey_clusters_local, storedmodel_directory + cluster_model_file + "_JourneyClusters")
cluster_json = journey_clusters.map(Data.extract_journey_json).collect()
with open(cluster_json_directory + "clusters.json", "w") as f:
for cluster in cluster_json:
f.write(cluster + "\n")
journey_clusters.unpersist()
# Build initial classification models
init_class_models = journeys_with_id.mapValues(lambda data: Models.train_init_class_model(init_class_alg, init_class_feature_names, data)).collectAsMap()
joblib.dump(init_class_models, storedmodel_directory + init_class_model_file)
# Build online classification models
online_class_models = journeys_with_id.mapValues(lambda data: Models.train_online_class_model(online_class_alg, online_class_feature_names, data)).collectAsMap()
joblib.dump(online_class_models, storedmodel_directory + online_class_model_file)
sc.stop()