def mutual_info(sdf, colnames):
check_columns(sdf, colnames)
n = len(colnames)
probs = []
for i in range(n):
probs.append(distribution(sdf, colnames[i]))
res = np.zeros(shape=(n, n))
for i in range(n):
for j in range(i, n):
tdf = VectorAssembler(inputCols=[colnames[i], colnames[j]],
outputCol='__vectors').transform(sdf)
tdf = distribution(tdf, '__vectors')
tdf = disassemble(dense_to_array(tdf, '__col', '__features'),
'__features')
tdf = tdf.join(probs[i].toDF('__features_0', '__p0'),
on='__features_0')
tdf = tdf.join(probs[j].toDF('__features_1', '__p1'),
on='__features_1')
mi = tdf.select(
F.sum(
F.expr(
'log2(__probability / (__p0 * __p1)) * __probability'))
).take(1)[0][0]
res[i, j] = mi
res[j, i] = mi
return pd.DataFrame(res, index=colnames, columns=colnames)
def annotate_pval_dataset(self, cur_df):
import pyspark
try:
tr_inst = self.spark.read.parquet(self.training_temp_dir)
te_inst = self.spark.read.parquet(self.testing_temp_dir)
return tr_inst, te_inst
except pyspark.sql.utils.AnalysisException as ex:
template = "An exception of type {0} occurred. Arguments:\n{1!r}"
message = template.format(type(ex).__name__, ex.args)
self.logger.info(message)
self.logger.info("PROCESS")
self.logger.debug("NOTEXISTS ANNOTATE_FILE")
self.logger.debug("RUN_PROCESS")
except:
self.logger.info("TEST_PURPOSE")
from pyspark.ml.feature import VectorAssembler
postfix = self.postfix.format(self.sel_top)
obs_df = cur_df
cur_cols = obs_df.columns
for i in self.non_feature_column:
cur_cols.remove(i)
self.logger.debug("feature_columns")
cur_cols = sorted(cur_cols)
self.logger.debug(cur_cols)
import json
json.dump({"non_demo_features": cur_cols},
open(self.json_feature_dump_loc, "w"))
obs_df = VectorAssembler(
inputCols=cur_cols, outputCol="features_imputed").transform(obs_df)
cur_time_list = obs_df.select("ID", "TIME_SPAN")
of_annotated = obs_df
of_excl_training = dict()
demo_feature = self.add_demo()
of_annotated = VectorAssembler(
inputCols=["features_imputed", "demo_feature"],
outputCol="features").transform(
of_annotated.join(demo_feature, "ID"))
of_annotated.show()
from pyspark.sql.functions import col, lit, when
self.logger.debug("ANNOTATED")
cur_test_ids = self.get_target_test_id()
self.logger.debug(cur_test_ids)
# TODO CHECK why I put 'why 0' comment over here?
self.logger.debug(len(cur_test_ids))
tr_inst, te_inst = self.cur_annotator.prep_TR_TE(
of_annotated, test_id_list=cur_test_ids)
self.logger.debug("IDS")
self.logger.debug(
tr_inst.select("ID").distinct().count(),
te_inst.select("ID").distinct().count())
self.logger.debug("TR_TE_CNT:{0}_{1}".format(tr_inst.count(),
te_inst.count()))
train_data_ID = tr_inst.select("ID").distinct().rdd.flatMap(
list).collect()
testing_data_ID = te_inst.select("ID").distinct().rdd.flatMap(
list).collect()
self.action_df.show()
train_action_df = self.action_df.where(
col("ID").isin(train_data_ID)).persist()
self.logger.debug(train_action_df.select("ID").distinct().count())
train_terminal_outcome = self.terminal_outcome.where(
col("ID").isin(train_data_ID)).persist()
self.logger.debug(
train_terminal_outcome.select("ID").distinct().count())
intv_w_p_val = self.identify_relevant_action(
train_action_df, train_terminal_outcome,
tr_inst.select("ID").distinct().count())
intv_w_p_val.join(
self.def_df.where(col("SOURCE").isin(["CPT", "MED", "PROC"])),
self.itemid).orderBy("p_val").show(100, truncate=False)
from pyspark.sql.functions import sum, rand, max, lit
from pyspark.ml.feature import VectorAssembler
cur_annot_topk = self.sel_top
self.action_df.show()
self.terminal_outcome.show()
annot_df = self.action_df.join(self.terminal_outcome, "ID").persist()
annot_df.show()
pos_inst_dict = dict()
from pyspark.sql.functions import count
for cur_of in [self.target_disch_col]:
# For debug purpose, pass if target_of is not identified
self.logger.debug(cur_of)
intv_w_p_val.where("DISCH_DX == '{0}'".format(cur_of)).orderBy(
col("p_val").cast("double")).show(50, truncate=False)
target_annot_criteria = intv_w_p_val.where(
"DISCH_DX == '{0}'".format(cur_of)).orderBy(
col("p_val").cast("double")).limit(cur_annot_topk)
target_annot_criteria.write.save(self.annot_intv_dir.format(
cur_of, cur_annot_topk),
mode="overwrite")
target_annot_criteria = target_annot_criteria.select(
self.itemid).rdd.flatMap(list).collect()
if len(target_annot_criteria) == 0:
self.logger.info(
"NO TERMINAL DX {0} idenfieid from pts".format(cur_of))
pos_inst_dict[cur_of] = None
continue
self.logger.debug(target_annot_criteria)
self.logger.debug(len(target_annot_criteria))
self.logger.debug("selected intv!!")
self.def_df.where(col(
self.itemid).isin(target_annot_criteria)).show(cur_annot_topk,
truncate=False)
pos_inst_dict[cur_of] = annot_df.where((col(self.itemid).isin(target_annot_criteria)) & (col("DISCH_DX") == cur_of))\
.select("ID", col("TIME_OBS").cast("date").alias("TIME_OBS"), lit("1").cast("double").alias("{0}_label".format(cur_of)))\
.distinct().persist()
pos_inst_dict[cur_of].groupBy("{0}_label".format(cur_of)).agg(
count("*")).show()
from pyspark.sql.functions import broadcast
true_inst = annot_df.where(
(col(self.itemid).isin(target_annot_criteria))
& (col("DISCH_DX") == cur_of))
excl_id = annot_df.withColumn("IS_TARGET_OF",when(col("DISCH_DX") ==cur_of,lit("1").cast("double")).otherwise(lit("0").cast("double")))\
.withColumn("IS_REL_INTV", when(col(self.itemid).isin(target_annot_criteria), lit("1").cast("double")).otherwise(lit("0").cast("double")))\
.groupBy("ID").agg(sum("IS_TARGET_OF").alias("SUM_IS_TARGET_OF"),sum("IS_REL_INTV").alias("SUM_IS_REL_INTV"))\
.where("(SUM_IS_TARGET_OF <> 0) AND (SUM_IS_REL_INTV == 0)").select("ID").distinct().rdd.flatMap(list).collect()
self.logger.debug("NUM_PTS_EXCLUDED:{0}".format(len(excl_id)))
self.logger.debug("TRAINING_INST_COUNT:{0}".format(
tr_inst.count()))
tr_inst = tr_inst.withColumn("TIME_OBS",col("TIME_SPAN.TIME_TO").cast("date"))\
.withColumn("{0}_excl".format(cur_of), col("ID").isin(excl_id).cast("double")).repartition("ID","TIME_OBS")\
.join(broadcast(pos_inst_dict[cur_of]),["ID","TIME_OBS"],"left_outer").fillna(value=0.0,subset=["{0}_label".format(cur_of)]).persist()
print(tr_inst.count())
tr_inst.groupBy("{0}_label".format(cur_of),
"{0}_excl".format(cur_of)).agg(count("*")).show()
te_inst = te_inst.withColumn("TIME_OBS",col("TIME_SPAN.TIME_TO").cast("date"))\
.withColumn("{0}_excl".format(cur_of), col("ID").isin(excl_id).cast("double")).repartition("ID","TIME_OBS")\
.join(broadcast(pos_inst_dict[cur_of]),["ID","TIME_OBS"],"left_outer").fillna(value=0.0, subset=["{0}_label".format(cur_of)]).persist()
print(te_inst.count())
te_inst.groupBy("{0}_label".format(cur_of),
"{0}_excl".format(cur_of)).agg(count("*")).show()
tr_inst.groupBy("ID").agg(
max("{0}_label".format(cur_of)).alias(
"{0}_label".format(cur_of)),
max("{0}_excl".format(cur_of)).alias(
"{0}_excl".format(cur_of))).groupBy(
"{0}_label".format(cur_of),
"{0}_excl".format(cur_of)).agg(count("*")).show()
te_inst.groupBy("ID").agg(
max("{0}_label".format(cur_of)).alias(
"{0}_label".format(cur_of)),
max("{0}_excl".format(cur_of)).alias(
"{0}_excl".format(cur_of))).groupBy(
"{0}_label".format(cur_of),
"{0}_excl".format(cur_of)).agg(count("*")).show()
tr_inst.write.save(self.training_temp_dir, mode="overwrite")
te_inst.write.save(self.testing_temp_dir, mode="overwrite")
tr_inst = self.spark.read.parquet(self.training_temp_dir)
te_inst = self.spark.read.parquet(self.testing_temp_dir)
#te_inst.show()
return (tr_inst, te_inst)
