def query_random_sample(self,
data_frame,
table_name,
query_str,
sample_per=0.1):
"""
get query data from spark
:param table_name: name of table you want to get data
:param query_str: sql strings
:return: query result as json Object
"""
try:
sc = self.spark_session_create("query_radom_sample")
tfmsa_logger("start query data !")
hdfs_path = settings.HDFS_DF_ROOT + "/" + data_frame + "/" + table_name
sqlContext = SQLContext(sc)
df = sqlContext.read.load(hdfs_path, "parquet")
df.registerTempTable(table_name)
result = sqlContext.sql(str(query_str)).sample(False,
float(sample_per),
seed=0).collect()
return result
except Exception as e:
tfmsa_logger(e)
raise Exception(e)
finally:
df.unpersist()
sqlContext.clearCache()
sqlContext.dropTempTable(table_name)
sc.clearFiles()
sc.stop()
tfmsa_logger("stop context")
def get_distinct_dataframe(self, data_frame, table_name, columns):
"""
get distinct table columns
:param table_name: name of table you want to get data
:param query_str: sql strings
:return: query result as json Object
"""
try:
sc = self.spark_session_create("get_distinct_dataframe")
tfmsa_logger("start find distinct column !")
hdfs_path = settings.HDFS_DF_ROOT + "/" + data_frame + "/" + table_name
query_str = "select * from " + table_name
sqlContext = SQLContext(sc)
df = sqlContext.read.load(hdfs_path, "parquet")
df.registerTempTable(table_name)
result = sqlContext.sql(str(query_str))
return_data = {}
for column in columns:
return_data[column.encode("UTF8")] = result.select(column).map(
lambda x: str(x[0]).encode("UTF8")).distinct().collect()
tfmsa_logger("End find distinct column !")
return return_data
except Exception as e:
tfmsa_logger(e)
raise Exception(e)
finally:
df.unpersist()
sqlContext.clearCache()
sqlContext.dropTempTable(table_name)
sc.clearFiles()
sc.stop()
tfmsa_logger("stop context")
def process(rdd):
"""
:param rdd: (user_id, movie_id)
:return: (user_id, [movie_id, ...])
"""
if rdd.isEmpty():
return rdd
spark = getSparkSessionInstance(rdd.context.getConf())
ssc = SQLContext(spark.sparkContext)
# 80
# update recommend table with newest; create dataframe of recommend table
tmp = rdd.collect()
for item in tmp:
collect.update_one({"user_id": item[0]},
{"$addToSet": {
"movie_list": item[1]
}})
user_list = rdd.map(lambda x: [x[0]]).reduce(reducer)
tmp_list = []
for item in collect.find({'user_id': {'$in': user_list}}):
tmp_list.append((item['user_id'], item['movie_list']))
tmp_df = ssc.createDataFrame(tmp_list, ['user_id', 'movie_list'])
ssc.registerDataFrameAsTable(tmp_df, 'recommend')
# 82: get the whole recommend list of incoming user
input_rdd = sc.parallelize(tmp_list).flatMap(mapper3)
input_df = spark.createDataFrame(input_rdd, ['user_id', 'movie_id'])
ssc.registerDataFrameAsTable(input_df, 'input_rating')
current_rdd = ssc.sql('''SELECT i.user_id, s.*
FROM input_rating i, similar s
WHERE i.movie_id == s.popular_id ''').rdd
# 86
total_rdd = current_rdd.flatMap(mapper7)\
.reduceByKey(reducer)\
.flatMap(mapper8)
# 88: sort the recommend list with avg_rating
recommend_df = spark.createDataFrame(total_rdd, ['user_id', 'movie_id'])
ssc.registerDataFrameAsTable(recommend_df, 'user_new')
movie_rating_df = ssc.sql(
'''SELECT u.user_id, u.movie_id, m.rating_avg, m.title
FROM user_new u, movies m
WHERE u.movie_id == m.movie_id''')
movie_rating_rdd = movie_rating_df.rdd\
.map(tuple)\
.map(mapper4)\
.reduceByKey(reducer)\
.map(mapper5)
# 90: filter out recommend items that are already in table recommend.movie_list
# truncate to RECOMMEND_LENGTH
movie_rating_df2 = ssc.createDataFrame(movie_rating_rdd,
['user_id', 'sort_list'])
ssc.registerDataFrameAsTable(movie_rating_df2, 'sorted')
movie_list_rdd = ssc.sql('''SELECT s.user_id, s.sort_list, r.movie_list
FROM sorted s, recommend r
WHERE s.user_id == r.user_id''').rdd
filtered_movie_rdd = movie_list_rdd.map(mapper6)
recommend_list = filtered_movie_rdd.map(mapper9).collect()
# 92
for item in recommend_list:
collect.update_one({"user_id": item[0]},
{"$set": {
"recommend_list": item[1]
}})
# clean up temporary tables
ssc.dropTempTable('input_rating')
ssc.dropTempTable('user_new')
ssc.dropTempTable('recommend')
ssc.dropTempTable('sorted')
return filtered_movie_rdd
def sql_context_api(spark):
sc = spark.sparkContext
sqlContext = SQLContext(sc)
print("Start running SQL context API")
# createDataFrame
l = [('Alice', 1)]
sqlContext.createDataFrame(l).collect()
res = sqlContext.createDataFrame(l, ['name', 'age']).collect()
print(res)
rdd = sc.parallelize(l)
sqlContext.createDataFrame(rdd).collect()
df = sqlContext.createDataFrame(rdd, ['name', 'age'])
res = df.collect()
print(res)
print("createDataFrame API finished")
# table and cache
df = spark.createDataFrame([('Alice', 5, 80), ('Alice', 5, 80),
('Alice', 10, 80)], ["name", "age", "height"])
sqlContext.registerDataFrameAsTable(df, "table1")
sqlContext.cacheTable("table1")
sqlContext.uncacheTable("table1")
sqlContext.cacheTable("table1")
sqlContext.clearCache()
# sqlContext.createExternalTable("table1", schema = df2)
sqlContext.dropTempTable("table1")
# res = df2.collect()
# print(res)
print("External, TempTable and cache API finished")
# getConf
res = sqlContext.getConf("spark.sql.shuffle.partitions")
print(res)
res = sqlContext.getConf("spark.sql.shuffle.partitions", u"10")
print(res)
sqlContext.setConf("spark.sql.shuffle.partitions", u"50")
res = sqlContext.getConf("spark.sql.shuffle.partitions", u"10")
print(res)
print("getConf API finished")
# newSession
newspark = sqlContext.newSession()
print("newSession API finished")
# range
res = sqlContext.range(1, 7, 2).collect()
print(res)
res = sqlContext.range(3).collect()
print(res)
print("range API finished")
# read
res = sqlContext.read
# text_sdf = sqlContext.readStream.text(tempfile.mkdtemp())
# res = text_sdf.isStreaming
# print(res)
print("read and readStream API finished")
# register
# sql
df = spark.createDataFrame([('Alice', 5, 80), ('Alice', 5, 80),
('Alice', 10, 80)], ["name", "age", "height"])
sqlContext.registerDataFrameAsTable(df, "table1")
df2 = sqlContext.sql("SELECT name AS f1, age as f2 from table1")
res = df2.collect()
print(res)
print("sql API finished")
# table
df = spark.createDataFrame([('Alice', 5, 80), ('Alice', 5, 80),
('Alice', 10, 80)], ["name", "age", "height"])
sqlContext.registerDataFrameAsTable(df, "table1")
df2 = sqlContext.table("table1")
res = (sorted(df.collect()) == sorted(df2.collect()))
print(res)
print("table API finished")
# tableNames
df = spark.createDataFrame([('Alice', 5, 80), ('Alice', 5, 80),
('Alice', 10, 80)], ["name", "age", "height"])
sqlContext.registerDataFrameAsTable(df, "table1")
res = ("table1" in sqlContext.tableNames())
print(res)
res = ("table1" in sqlContext.tableNames("default"))
print(res)
print("tableNames API finished")
# tables
sqlContext.registerDataFrameAsTable(df, "table1")
df2 = sqlContext.tables()
res = df2.filter("tableName = 'table1'").first()
print(res)
print("tables API finished")
print("Finish running SQL context API")
# In[9]:
#change dataframe to a table
temp.registerTempTable('df')
# In[10]:
#use sql command to deal with table defined
sqlContext = SQLContext(sc)
temp = sqlContext.sql(
"select *,(case when activity = '送礼物' then 12 when activity='评论' then 10 when activity='播放' then 2 when activity='下载' then 6 end) as score from df"
)
# In[31]:
sqlContext.dropTempTable("df")
# In[13]:
data = temp.groupby(['user_id_x', 'user_work_id']).agg({'score': 'sum'})
data.show(5)
# In[43]:
data = data.withColumnRenamed("sum(score)", "score")
# In[61]:
data.count()
# In[46]:
def sql_context_api(spark):
sc = spark.sparkContext
sqlContext = SQLContext(sc)
print("Start running SQL context API")
# createDataFrame
l = [('Alice', 1)]
sqlContext.createDataFrame(l).collect()
res = sqlContext.createDataFrame(l, ['name', 'age']).collect()
print(res)
rdd = sc.parallelize(l)
sqlContext.createDataFrame(rdd).collect()
df = sqlContext.createDataFrame(rdd, ['name', 'age'])
res = df.collect()
print(res)
print("createDataFrame API finished")
# table and cache
df = spark.createDataFrame([('Alice', 5, 80), ('Alice', 5, 80),
('Alice', 10, 80)], ["name", "age", "height"])
sqlContext.registerDataFrameAsTable(df, "table1")
sqlContext.cacheTable("table1")
sqlContext.uncacheTable("table1")
sqlContext.cacheTable("table1")
sqlContext.clearCache()
# sqlContext.createExternalTable("table1", schema = df2)
sqlContext.dropTempTable("table1")
# res = df2.collect()
# print(res)
print("External, TempTable and cache API finished")
# getConf
res = sqlContext.getConf("spark.sql.shuffle.partitions")
print(res)
res = sqlContext.getConf("spark.sql.shuffle.partitions", u"10")
print(res)
sqlContext.setConf("spark.sql.shuffle.partitions", u"50")
res = sqlContext.getConf("spark.sql.shuffle.partitions", u"10")
print(res)
print("getConf API finished")
# newSession
newspark = sqlContext.newSession()
print("newSession API finished")
# range
res = sqlContext.range(1, 7, 2).collect()
print(res)
res = sqlContext.range(3).collect()
print(res)
print("range API finished")
# read
res = sqlContext.read
text_sdf = sqlContext.readStream.text(
"/ppml/trusted-big-data-ml/work/examples/helloworld.py")
res = text_sdf.isStreaming
print(res)
print("read and readStream API finished")
# sql
df = spark.createDataFrame([('Alice', 5, 80), ('Alice', 5, 80),
('Alice', 10, 80)], ["name", "age", "height"])
sqlContext.registerDataFrameAsTable(df, "table1")
df2 = sqlContext.sql("SELECT name AS f1, age as f2 from table1")
res = df2.collect()
print(res)
print("sql API finished")
# table
df = spark.createDataFrame([('Alice', 5, 80), ('Alice', 5, 80),
('Alice', 10, 80)], ["name", "age", "height"])
sqlContext.registerDataFrameAsTable(df, "table1")
df2 = sqlContext.table("table1")
res = (sorted(df.collect()) == sorted(df2.collect()))
print(res)
print("table API finished")
# tableNames
df = spark.createDataFrame([('Alice', 5, 80), ('Alice', 5, 80),
('Alice', 10, 80)], ["name", "age", "height"])
sqlContext.registerDataFrameAsTable(df, "table1")
res = ("table1" in sqlContext.tableNames())
print(res)
res = ("table1" in sqlContext.tableNames("default"))
print(res)
print("tableNames API finished")
# tables
sqlContext.registerDataFrameAsTable(df, "table1")
df2 = sqlContext.tables()
res = df2.filter("tableName = 'table1'").first()
print(res)
print("tables API finished")
# register
# strlen = sqlContext.registerFunction("stringLengthString", lambda x: len(x))
# res = spark.sql("SELECT stringLengthString('test')").collect()
# print(res)
spark.udf.registerJavaFunction("javaStringLength3",
"org.apache.spark.sql.JavaStringLength",
"integer")
res = spark.sql("SELECT javaStringLength3('test')").collect()
print(res)
print("register API finished")
print("Finish running SQL context API")
StructField("age", IntegerType(), True)
])
res3 = sqlContext.createDataFrame(rdd, schema)
row = res3.collect()
print("xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 5th xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")
print(row)
# -------------------------------------------- 现在该示例未能执行成功，错误消息为：ImportError: No module named pandas
# row = sqlContext.createDataFrame(df.toPandas()).collect()
# print("xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 6th xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")
# print(row)
# -------------------------------------------- createExternalTable(tableName, path=None, source=None, schema=None, **options) and drop it
print(
"xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 7th xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
)
sqlContext.registerDataFrameAsTable(df, "stu5")
sqlContext.dropTempTable("stu5")
# ---------------------------------------------- json file 待测
# ----------------------------------------------
print(
"xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 8th xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
)
sqlContext.registerDataFrameAsTable(df, "stu6")
df2 = sqlContext.sql("SELECT name AS f1, age as f2 from stu6")
print(df2.collect())
sqlContext.table("stu6")
print(sorted(df.collect()) == sorted(df2.collect())) # True
# -------------------------------------------------------
print("stu6" in sqlContext.tableNames()) # True
print("stu3" in sqlContext.tableNames()) # False
# -------------------------------------------------------
sqlContext.registerDataFrameAsTable(df, "stu5")
def main(sc):
sqlContext = SQLContext(sc)
# In[1]:
input_path = ''
model_path = ''
model_info_path = model_path + ''
model_scaler_path = model_path + ''
model_train_set_path = model_path + ''
# Import the table of features and labels into dataframes
df_data = sqlContext.read.format('com.databricks.spark.csv').options(
header='true', inferschema='true').load(input_path)
# Convert all features to double type except for ID and Label, which remain as strings
# This is done because the Random Forest Algorithm requires features to be numbers
df_data = df_data.select(
*(col(c).cast("double").alias(c) for c in df_data.columns[1:-1]),
df_data.u_msisdn.cast('string'), df_data.tag.cast('string'))
# Defines that the first column is the unique ID, the last one contains the labels and all the ones in between are the given features
df_master = df_data.rdd.map(lambda r: Row(
cust_id=r[-2], label=r[-1], features=Vectors.dense(r[:-2]))).toDF()
# Randomly Split the data into a test and train set
(df_master_train, df_master_test) = df_master.randomSplit([0.5, 0.5],
seed=123)
# Set the Random Forest input to the training set
rf_init_data = df_master_train
# Indexing labels for Random Forest Algorithm
labelIndexer = StringIndexer(inputCol="label", outputCol="indexed_label")
model = labelIndexer.fit(rf_init_data)
rf_init_data = model.transform(rf_init_data)
# Indexing features for Random Forest Algorithm
featureIndexer = VectorIndexer(inputCol="features",
outputCol="indexed_features",
maxCategories=2)
model = featureIndexer.fit(rf_init_data)
rf_init_data = model.transform(rf_init_data)
# Configures inbuilt Random Forest Classifier function with 500 trees,
# max depth = 8 and 32 bins
rf_init = RandomForestClassifier(labelCol="indexed_label",
featuresCol="indexed_features",
numTrees=500,
impurity="gini",
maxDepth=8,
maxBins=32)
rf_init_data.persist() # Cache the data set
rf_init_model = rf_init.fit(
rf_init_data) # Run the Random Forest Algorithm
rf_init_data.unpersist()
# Extract a list of feature importances from the output of the Random Forest
# Algorithm with each element corresponding to a feature
rf_init_varimp = np.sqrt(rf_init_model.featureImportances.toArray())
# Creates a list containing the 6 most important features to be used later
# to subset our entire data from 146 features to just 6!
# Create a list containing the names of all features
column_names = df_data.columns[:-2]
#Creating a dictionary mapping feature names to their respective importances
NameToImp = dict()
for i in range(len(column_names)):
key = column_names[i]
value = rf_init_varimp[i]
NameToImp[key] = value
# Sorted list in reverse order according to the variable importances
sorted_varimp = sorted(NameToImp.values(), reverse=True)
# Collect importances of 6 most important features
sorted_top_varimp = sorted_varimp[:6]
# Sorted list of column names in reverse order according to varimp
sorted_colnames = sorted(NameToImp, key=NameToImp.get, reverse=True)
# Collect colnames of 6 most imp features
col_names = sorted_colnames[:6]
# Pulling data for most import 6 features
df_data_new = df_data.select(
df_data.u_msisdn.cast('string'), df_data.tag.cast('string'),
*(col(c).cast("double").alias(c) for c in col_names))
# Defines that the first column is the unique ID, the last one contains the labels and all the ones in between are the given features
df_master_new = df_data_new.rdd.map(lambda r: Row(
cust_id=r[0], label=r[1], features=Vectors.dense(r[2:]))).toDF()
# Scale and normaize the features so that all features can be compared
# and create a new column for the features
scaler = StandardScaler(inputCol="features",
outputCol="scaled_features",
withStd=True,
withMean=True)
# Compute summary statistics by fitting the StandardScaler
scalerModel = scaler.fit(df_master_new)
# Normalize each feature to have unit standard deviation.
df_master_new = scalerModel.transform(df_master_new)
#The old features have been replaced with their scaled versions and thus
# we no longer care about the old, unbalanced features
df_master_new = df_master_new.drop('features')
# Randomly Split the data into a test and train set
(df_master_train, df_master_test) = df_master_new.randomSplit([0.5, 0.5],
seed=123)
test_all = df_master_test
sqlContext.registerDataFrameAsTable(df_master_train,
"df_master_train_table")
# Remove the negative labels as only the positive ones are important
train_all = sqlContext.sql(
'select * from df_master_train_table where label = 1')
# Multiply feature values with corresponding importances
m = ElementwiseProduct(scalingVec=Vectors.dense(sorted_top_varimp),
inputCol="scaled_features",
outputCol="scaled_weighted_features")
train_all = m.transform(train_all)
test_all = m.transform(test_all)
sqlContext.dropTempTable("df_master_train_table")
# Create a list of tasks containing tuples of number of neighbours and
# cutoff frequencies to be passed to KNN algorithm
number_of_neighbours = [250, 550, 750, 1000]
popshared = 0.30
num_indices = int(popshared * (test_all.count()))
tasks = []
for num_neighbour in number_of_neighbours:
tasks = tasks + [(num_neighbour, num_indices)]
# Partitioning the tasks for parallel processing
tasksRDD = sc.parallelize(tasks, numSlices=len(tasks))
tasksRDD.collect()
train_pd = train_all.toPandas()
test_pd = test_all.toPandas()
train_pd['indices'] = train_pd.index
test_pd['indices'] = test_pd.index
# Converting features into SparseVector format
l_train = list()
for k in train_pd.scaled_weighted_features:
l_train.append(
Vectors.sparse(len(k),
[(i, j) for i, j in enumerate(k) if j != 0]))
l_test = list()
for k in test_pd.scaled_weighted_features:
l_test.append(
Vectors.sparse(len(k),
[(i, j) for i, j in enumerate(k) if j != 0]))
# Converting to a numpy array
knn_train = np.asarray(l_train)
knn_test = np.asarray(l_test)
# Broadcasting the training and test sets to all partitions
train_broadcast = sc.broadcast(knn_train)
test_broadcast = sc.broadcast(knn_test)
# Calling K Nearest Neighbour search on each partition
tree_type = "kd_tree"
resultsRDD = tasksRDD.map(lambda nc: findNearestNeighbour(
train_broadcast, test_broadcast, nc[0], nc[1], test_pd, tree_type))
resultsRDD.cache()
resultsRDD.count()
resultsPD = resultsRDD.toDF().toPandas()
resultsPD["popshared"] = popshared
resultsPD = resultsPD.rename(columns={'_1': 'Recall'})
resultsPD = resultsPD.rename(columns={'_2': 'Number of Neighbors'})
bestResult = (resultsPD.sort_values(by=["Recall"], ascending=[0])).iloc[0]
bestNN = int(bestResult["Number of Neighbors"])
bestRecall = bestResult["Recall"]
# saving the model info - varimp,recall,NN,col_names to model_path
column_names = [i for i in col_names]
model_info = sc.parallelize([{
"varimp": sorted_top_varimp,
"recall": bestRecall,
"NN": bestNN,
"col_names": column_names
}])
model_info.saveAsPickleFile(path=model_info_path)
# saving the scaler model to model_path
scalerModel.write().overwrite().save(model_scaler_path)
# saving the train set to model_path
df_master_new.rdd.saveAsPickleFile(path=model_train_set_path)
sqlContext.registerDataFrameAsTable(dfsub1, 'temp')
df2 = sqlContext.sql("""SELECT ActionGeo_CountryCode,
CAST(SQLDATE AS INTEGER), CAST(MonthYear AS INTEGER), CAST(Year AS INTEGER),
CASE WHEN Actor1Type1Code = '' AND Actor2Type1Code <> '' THEN Actor2Type1Code
ELSE Actor1Type1Code END AS Actor1Type1Code,
CAST(NumArticles AS INTEGER),
CAST(GoldsteinScale AS INTEGER),
CAST(AvgTone AS INTEGER)
FROM temp
WHERE ActionGeo_CountryCode <> '' AND ActionGeo_CountryCode IS NOT NULL
AND Actor1Type1Code <> '' AND Actor1Type1Code IS NOT NULL
AND NumArticles <> '' AND NumArticles IS NOT NULL
AND GoldsteinScale <> '' AND GoldsteinScale IS NOT NULL
AND AvgTone <> '' AND AvgTone IS NOT NULL""")
sqlContext.dropTempTable('temp')
sqlContext.registerDataFrameAsTable(df2, 'temp3')
sqlContext.cacheTable('temp3')
dfdaily = sqlContext.sql("""SELECT ActionGeo_CountryCode,
SQLDATE,
Actor1Type1Code,
SUM(NumArticles) AS NumArticles,
ROUND(AVG(GoldsteinScale),0) AS GoldsteinScale,
ROUND(AVG(AvgTone),0) AS AvgTone
FROM temp3
GROUP BY ActionGeo_CountryCode,
SQLDATE,
Actor1Type1Code""")
dfmonthly = sqlContext.sql("""SELECT ActionGeo_CountryCode,
# In[40]:
sqlContext.createDataFrame(rdd).collect()
# In[41]:
df = sqlContext.createDataFrame(rdd, ['name', 'age'])
# In[42]:
df.collect()
# In[43]:
sqlContext.registerDataFrameAsTable(df, "table1")
sqlContext.dropTempTable("table1")
# In[44]:
sqlContext.getConf("spark.sql.shuffle.partitions")
# In[45]:
sqlContext.getConf("spark.sql.shuffle.partitions", u"10")
# In[46]:
sqlContext.setConf("spark.sql.shuffle.partitions", u"50")
# In[47]:
def main(sc):
sqlContext = SQLContext(sc)
input_path = ''
output_path = ''
model_path = ''
model_info_path = model_path + ''
model_scaler_path = model_path + ''
model_train_set_path = model_path + ''
#IMPORT THE CLIENT DATA
client_data = sqlContext.read.format('com.databricks.spark.csv').options(
header='true', inferschema='true').load(input_path)
# Load the models and train data from Training Interface paths
model_info = sc.pickleFile(model_info_path).flatMap(
lambda x: x.items()).collectAsMap()
scalerModel = StandardScalerModel.load(model_scaler_path)
df_master_new = sc.pickleFile(model_train_set_path).toDF()
col_names = model_info['col_names']
sorted_top_varimp = model_info['varimp']
# Pulling data for most import 6 features
client_data = client_data.select(
client_data.u_msisdn.cast('string'),
*(col(c).cast("double").alias(c) for c in col_names))
# Defines that the first column is the unique ID, the last one contains the labels and all the ones in between are the given features
client_master = client_data.rdd.map(
lambda r: Row(cust_id=r[0], features=Vectors.dense(r[1:]))).toDF()
# Scale and normaize the features so that all features can be compared
# and create a new column for the features
client_scaler = StandardScaler(inputCol="features",
outputCol="scaled_features",
withStd=True,
withMean=True)
# Compute summary statistics by fitting the StandardScaler
scalerModel = client_scaler.fit(client_master)
# Normalize each feature to have unit standard deviation.
client_master = scalerModel.transform(client_master)
#The old features have been replaced with their scaled versions and thus
# we no longer care about the old, unbalanced features
client_master = client_master.drop('features')
sqlContext.registerDataFrameAsTable(df_master_new, "df_master_train_table")
# Remove the negative labels as only the positive ones are important
train_all_client = sqlContext.sql(
'select * from df_master_train_table where label = 1')
# Multiply feature values with corresponding importances
m = ElementwiseProduct(scalingVec=Vectors.dense(sorted_top_varimp),
inputCol="scaled_features",
outputCol="scaled_weighted_features")
train_all_client = m.transform(train_all_client)
client_master = m.transform(client_master)
sqlContext.dropTempTable("df_master_train_table")
nn = 1000
popshared = 0.30
num_indices = (int)(popshared * client_master.count())
tree_type = "kd_tree"
nn, popshared, num_indices
train_pd = train_all_client.toPandas()
test_pd = client_master.toPandas()
freq_table = findNearestNeighbour_client(train_pd, test_pd, nn,
num_indices, tree_type)
sqlContext.createDataFrame(freq_table[['cust_id', 'freq']], ).repartition(
1).write.format("com.databricks.spark.csv").save(output_path)
def drop_table(self, table_name):
u" 删除 table表"
SQLContext.dropTempTable(table_name)
