def test_string(self):
lr = LogisticRegression()
for col in ['features', u'features', np.str_('features')]:
lr.setFeaturesCol(col)
self.assertEqual(lr.getFeaturesCol(), 'features')
self.assertRaises(TypeError,
lambda: LogisticRegression(featuresCol=2.3))
def test_string(self):
lr = LogisticRegression()
for col in ['features', u'features', np.str_('features')]:
lr.setFeaturesCol(col)
self.assertEqual(lr.getFeaturesCol(), 'features')
self.assertRaises(TypeError, lambda: LogisticRegression(featuresCol=2.3))
def gen_lr_sort_model(self):
self.spark_session.sql("use portal")
# 用户文章点击行为
sql = "select user_id, article_id, channel_id, click_flag from t_user_behaviour"
user_article_click_df = self.spark_session.sql(sql)
user_article_click_df.show()
# 获取用户画像 基础数据、偏好喜好数据
sql = "select split(user_id, ':')[1] user_id, basic_info.gender, basic_info.age, preference_info " \
"from t_user_profile"
user_profile_df = self.spark_session.sql(sql)
user_profile_df.show()
user_article_click_df = user_article_click_df.join(user_profile_df, on=["user_id"], how="left")
# 抽取文章所属频道关键词向量特征
def extract_channel_keyword_feature(partition):
from pyspark.ml.linalg import Vectors
for row in partition:
try:
weights = sorted([row.preference_info[key] for key in row.preference_info.keys()
if key.split(':')[0] == row.channel_id], reverse=True)[:10]
except Exception as e:
print e.message
weights = [0.0] * 10
yield row.article_id, row.channel_id, row.user_id, int(row.gender), int(row.age), \
Vectors.dense(weights if weights else [0.0] * 10), row.click_flag
user_article_click_df = user_article_click_df.rdd.mapPartitions(extract_channel_keyword_feature) \
.toDF(["article_id", "channel_id", "user_id", "gender", "age", "channel_weights", "click_flag"])
user_article_click_df.show()
# 获取文章画像
article_profile_df = self.spark_session.sql("select * from t_article_profile")
# 抽取文章关键词向量特征
def extract_feature(partition):
from pyspark.ml.linalg import Vectors
for row in partition:
try:
weights = sorted(row.keywords.values(), reverse=True)[:10]
except Exception as e:
print e.message
weights = [0.0] * 10
yield row.article_id, Vectors.dense(weights if weights else [0.0] * 10)
article_profile_df = article_profile_df.rdd.mapPartitions(extract_feature).toDF(["article_id", "article_weights"])
article_profile_df.show()
user_article_click_df = user_article_click_df.join(article_profile_df, on=["article_id"], how="inner")
user_article_click_df.show()
# 获取文章向量
article_vector_df = self.spark_session.sql("select article_id, vector from t_article_vector")
def array_to_vector(partition):
from pyspark.ml.linalg import Vectors
for row in partition:
yield row.article_id, Vectors.dense(row.vector)
article_vector_df = article_vector_df.rdd.mapPartitions(array_to_vector).toDF(["article_id", "article_vector"])
article_vector_df.show()
user_article_click_df = user_article_click_df.join(article_vector_df, on=["article_id"], how="inner")
user_article_click_df.show()
# 收集特征
from pyspark.ml.feature import VectorAssembler
input_cols = ["channel_id", "gender", "age", "channel_weights", "article_weights", "article_vector"]
user_article_click_df = VectorAssembler().setInputCols(input_cols) \
.setOutputCol("features") \
.transform(user_article_click_df)
user_article_click_df.show()
# Logistic Regression
from pyspark.ml.classification import LogisticRegression
logistic_regression = LogisticRegression()
logistic_regression_model = logistic_regression.setFeaturesCol("features") \
.setLabelCol("click_flag")\
.fit(user_article_click_df)
logistic_regression_model.write().overwrite().save(
"hdfs://192.168.0.1:9000/user/models/logistic_regression/lr.model")
from pyspark.ml.classification import LogisticRegressionModel
logistic_regression_model = LogisticRegressionModel.load(
"hdfs://192.168.0.1:9000/user/models/logistic_regression/lr.model")
logistic_regression_result = logistic_regression_model.transform(user_article_click_df)
logistic_regression_result.select(["click_flag", "probability", "prediction"]).show()
# ROC
def vector_to_double(row):
return float(row.click_flag), float(row.probability[1])
score_labels = logistic_regression_result.select(["click_flag", "probability"]).rdd.map(vector_to_double)
score_labels.collect()
from pyspark.mllib.evaluation import BinaryClassificationMetrics
binary_classification_metrics = BinaryClassificationMetrics(scoreAndLabels=score_labels)
area_under_roc = binary_classification_metrics.areaUnderROC
print area_under_roc
