def prepare_df_for_prediction(self, dataframe: DataFrame, label_to_predict,
categorical_features, continuous_features):
# stages = self.build_pipeline_stages(categorical_features, continuous_features)
# pipelined_dataframe = self.pipeline_dataframe(stages, dataframe)
hasher = FeatureHasher(
inputCols=[*categorical_features, *continuous_features],
outputCol='features')
featurized = hasher.transform(dataframe)
label_features = featurized.select(label_to_predict,
'features').withColumnRenamed(
label_to_predict, 'label')
return label_features
def ml_transformer(df,feature_all,response_feature):
'''
preprocess the data ready for the logistic regression model
'''
feature_only = feature_all.remove(response_feature)
hasher = FeatureHasher(inputCols=feature_only,outputCol="features")
df_featurized = hasher.transform(df)
df_train, df_test = df_featurized.randomSplit([0.8, 0.2], seed=12345)
df_size = float(df_train.select(response_feature).count())
num_positives = df_train.select(response_feature).where('{}==1'.format(response_feature)).count()
num_negatives = df_train.select(response_feature).where('{}==0'.format(response_feature)).count()
balance_ratio = 1- num_positives/df_size
df_train=df_train.withColumn("classWeights", when(df_train.response_feature == 1,balance_ratio).otherwise(1-balance_ratio))
return df_train,df_test
def featurehasher(request):
print("into featurehasher")
value = "featurehasher"
file_id = request.GET['fileid']
print(file_id)
spark = sparkSession(request)
print("Created Spark Session")
spark.sql('use hivedb')
formFile = get_object_or_404(CSVFile, id=file_id)
filePath = BASE_DIR + '\\' + str(formFile.file)
filename = filePath
projectid = formFile.project_fk.id
csvpath = filename
seperate = csvpath.split('/')
for temp in seperate:
pass
splitcsv = temp.split('.')
csvname = splitcsv[0]
fid = str(file_id)
pid = str(projectid)
tablename = csvname + '_' + fid + '_' + pid
print(tablename)
datapreprocess = tablename + '_prerocessing'
#spark.sql("select * from "+str(datapreprocess)+"").show()
df = spark.table(datapreprocess)
header = df.columns
print(header)
hasher = FeatureHasher(inputCols=header, outputCol="features")
featurized = hasher.transform(df)
featurized.show(truncate=False)
dff = featurized
scaler = StandardScaler(inputCol="features",
outputCol="scaledFeatures",
withStd=True,
withMean=False)
# Compute summary statistics by fitting the StandardScaler
scalerModel = scaler.fit(dff)
# Normalize each feature to have unit standard deviation.
scaledData = scalerModel.transform(dff)
scaledData.show(truncate=False)
return JsonResponse({"success": True}, status=200)
def create_featureHasher(input_col:str, nq:int) -> FeatureHasher:
"""
Create a Feature Hasher for a specified column
Uses as output colum the input + _encoded (creates oneHotEncodings for strings)
Parameters
----------
input_col: str
Name of the Input Column
nq: Int
Number of Quantiles to use
Return
------
FeatureHasher
"""
output_col = input_col + "_encoded"
return FeatureHasher(numFeatures=nq,
inputCols=[input_col],
outputCol=output_col)
result = model.transform(df)
result.show(truncate=False)
# COMMAND ----------
###Feature hashing
from pyspark.ml.feature import FeatureHasher
dataset = spark.createDataFrame([(2.2, True, "1", "foo"),
(3.3, False, "2", "bar"),
(4.4, False, "3", "baz"),
(5.5, False, "4", "foo")],
["real", "bool", "stringNum", "string"])
hasher = FeatureHasher(inputCols=["real", "bool", "stringNum", "string"],
outputCol="features")
featurized = hasher.transform(dataset)
featurized.show(truncate=False)
# COMMAND ----------
####Feature transformer (transforming sentences into words)
from pyspark.ml.feature import Tokenizer, RegexTokenizer
from pyspark.sql.functions import col, udf
from pyspark.sql.types import IntegerType
sentenceDataFrame = spark.createDataFrame(
[(0, "Hi I heard about Spark"), (1, "I wish Java could use case classes"),
(2, "Logistic,regression,models,are,neat")], ["id", "sentence"])
df.cache()
total_detections = df.select("HasDetections").where(df.HasDetections==1).count()
print("Total Rows: {0}".format(df.count()))
print("Total HasDetections: {0}".format(total_detections))
print("****** Crosstabulations ******")
df.crosstab("HasDetections", "SkuEdition").show(truncate=False)
df.crosstab("HasDetections", "ProductName").show(truncate=False)
df.crosstab("HasDetections", "AVProductsEnabled").show(truncate=False)
df.crosstab("HasDetections", "IsBeta").show(truncate=False)
df.crosstab("HasDetections", "Platform").show(truncate=False)
df.crosstab("HasDetections", "Census_DeviceFamily").show(truncate=False)
df.crosstab("HasDetections", "Census_OSInstallTypeName").show(truncate=False)
all_columns = df.columns
label_col = ["HasDetections"]
meta_cols = ["MachineIdentifier"]
#feature_cols = ["SkuEdition", "ProductName", "AVProductsEnabled", "IsBeta", "Platform", "Census_DeviceFamily", "Census_OSInstallTypeName"]
feature_cols = list(set(all_columns) - set(label_col) - set(meta_cols))
ordered_cols = list(label_col + meta_cols + feature_cols)
#hasher = FeatureHasher(numFeatures=len(feature_cols), inputCols=feature_cols, outputCol="features", categoricalCols=feature_cols)
hasher = FeatureHasher(numFeatures=len(feature_cols), inputCols=feature_cols, outputCol="features", categoricalCols=feature_cols)
df_features = df.sample(fraction=0.50, seed=3)
df_features = df.select(*ordered_cols)
df_features = hasher.transform(df_features)
chi_test = ChiSquareTest.test(df_features, "features", "HasDetections")
from __future__ import print_function
from pyspark.sql import SparkSession
# $example on$
from pyspark.ml.feature import FeatureHasher
# $example off$
if __name__ == "__main__":
spark = SparkSession\
.builder\
.appName("FeatureHasherExample")\
.getOrCreate()
# $example on$
dataset = spark.createDataFrame([
(2.2, True, "1", "foo"),
(3.3, False, "2", "bar"),
(4.4, False, "3", "baz"),
(5.5, False, "4", "foo")
], ["real", "bool", "stringNum", "string"])
hasher = FeatureHasher(inputCols=["real", "bool", "stringNum", "string"],
outputCol="features")
featurized = hasher.transform(dataset)
featurized.show(truncate=False)
# $example off$
spark.stop()
drop_cols = list(
set(df.columns) - set(meta_cols) - set(continuous_cols) -
set(categorical_cols) - set(feature_cols))
df = df.drop(*drop_cols)
df.cache()
print("Creating Splits")
train, test = df.randomSplit([0.7, 0.3])
print("Selected Features Count: {0}".format(len(feature_cols)))
print("Selected Features: {0}".format(feature_cols))
print("Building Pipeline")
categorical_hasher = FeatureHasher(inputCols=categorical_cols,
outputCol="categorical_features",
categoricalCols=categorical_cols)
continuous_vector = VectorAssembler(inputCols=continuous_cols,
outputCol="continuous_vector")
scaler = MinMaxScaler(min=0.0,
max=1.0,
inputCol=continuous_vector.getOutputCol(),
outputCol="continuous_features")
features = VectorAssembler(inputCols=feature_cols, outputCol="features")
classifier = LogisticRegression(regParam=0.01,
maxIter=100,
aggregationDepth=2,
fitIntercept=True,
family="binomial",
elasticNetParam=0.0)
one_rest = OneVsRest(classifier=classifier,
df = df.withColumnRenamed("click", "label")
df_train, df_test = df.randomSplit([0.7, 0.3], 42)
df_train.cache()
df_test.cache()
categorical = df_train.columns
categorical.remove('label')
print(categorical)
from pyspark.ml.feature import FeatureHasher
hasher = FeatureHasher(numFeatures=10000,
inputCols=categorical,
outputCol="features")
hasher.transform(df_train).select("features").show()
from pyspark.ml.classification import LogisticRegression
classifier = LogisticRegression(maxIter=20,
regParam=0.000,
elasticNetParam=0.000)
stages = [hasher, classifier]
from pyspark.ml import Pipeline
pipeline = Pipeline(stages=stages)
]
drop_cols = list(set(df.columns) - set(selected_cols) - set(meta_cols))
print("Performing Cleanup")
df = df.drop(*drop_cols)
df.cache()
print("Creating Splits")
train, test = df.randomSplit([0.7, 0.3])
print("Selected Features Count: {0}".format(len(selected_cols)))
print("Selected Features: {0}".format(selected_cols))
print("Building Pipeline")
hasher = FeatureHasher(numFeatures=1024,
inputCols=selected_cols,
outputCol="features",
categoricalCols=selected_cols)
forest = RandomForestClassifier(featuresCol="features",
labelCol="HasDetections",
predictionCol="prediction",
probabilityCol="probability")
pipeline = Pipeline(stages=[hasher, forest])
evaluator = MulticlassClassificationEvaluator(labelCol="HasDetections",
predictionCol="prediction",
metricName="accuracy")
print("Configuring Validation")
params = ParamGridBuilder() \
.addGrid(hasher.numFeatures, [1024]) \
.addGrid(forest.maxDepth, [30]) \
ratio = 1.0
counts = train.select(f'_c{label_idx}').groupBy(
f'_c{label_idx}').count().collect()
higher_bound = counts[1][1]
treshold = int(ratio * float(counts[0][1]) / counts[1][1] * higher_bound)
rand_gen = lambda x: randint(0, higher_bound) if x == 0 else -1
udf_rand_gen = udf(rand_gen, IntegerType())
train = train.withColumn('rand_idx', udf_rand_gen('_c0'))
train_subsample = train.filter(train['rand_idx'] < treshold)
train_subsample = train_subsample.drop('rand_idx')
train_subsample.select(f'_c{label_idx}').groupBy(
f'_c{label_idx}').count().show(n=5)
# パイプラインの構築
hasher = FeatureHasher(numFeatures=262144,
inputCols=real_features + category_features,
outputCol='features',
categoricalCols=category_features)
lr = LogisticRegression(featuresCol='features', labelCol=f'_c{label_idx}')
pipeline = Pipeline(stages=[hasher, lr])
model = pipeline.fit(train_subsample)
print(model.stages[-1].coefficients)
predictions = model.transform(test)
evaluator = MulticlassClassificationEvaluator(labelCol=f'_c{label_idx}',
metricName='f1')
f1 = evaluator.evaluate(predictions)
print(f'f1 = {f1}')
from pyspark.ml.feature import FeatureHasher
from pyspark.ml.stat import ChiSquareTest
from pyspark.ml.feature import ChiSqSelector
from pyspark.ml.linalg import Vectors
from pyspark.sql import Column
import pyspark.sql.functions as F
data = [(0, 2.0, True, "1", "foo"), (1, 3.0, False, "2", "bar"),
(0, 2.0, True, "1", "foo"), (1, 3.0, False, "2", "bar")]
cols = ["label", "real", "bool", "stringNum", "string"]
feature_cols = ["real", "bool", "stringNum", "string"]
x = spark.createDataFrame(data, cols)
h = FeatureHasher(numFeatures=4,
inputCols=feature_cols,
outputCol="features",
categoricalCols=feature_cols)
x_df = h.transform(x)
x_df.show(truncate=False)
s = ChiSqSelector(numTopFeatures=2,
labelCol="label",
featuresCol="features",
outputCol="selectedFeatures")
m = s.fit(x_df)
m_df = m.transform(x_df)
m_df.show(truncate=False)
s_df = m_df.select(*(m_df.columns[column_index]
for column_index in m.selectedFeatures))
s_df.show(truncate=False)
m.selectedFeatures
"AMT_PAYMENT").cache()
# joining to the master table
df_train = df_train.join(df_prev_app_means, ['SK_ID_CURR'], how='left').cache()
df_train = df_train.join(df_payment_means, ['SK_ID_CURR'], how='left').cache()
# filling nan values
df_train = df_train.na.fill(0).cache()
logger.info("# Rows:" + str(df_train.count()))
logger.info("# Cols:" + str(len(df_train.columns)))
labelIndexer = StringIndexer(inputCol="label",
outputCol="indexedLabel").fit(df_train)
labeled = labelIndexer.transform(df_train)
hasher = FeatureHasher(inputCols=[
column for column in list(set(df_train.columns)) if column != 'label'
],
outputCol="indexedFeatures",
numFeatures=len([
column for column in list(set(df_train.columns))
if column != 'label'
]))
featurized = hasher.transform(df_train)
# Split the data into training and test sets (30% held out for testing)
trainingData, testData = df_train.randomSplit([0.7, 0.3], seed=1234)
# Train a RandomForest model.
rf = RandomForestClassifier(labelCol="indexedLabel",
featuresCol="indexedFeatures",
numTrees=20,
maxDepth=15)
# Chain indexers and forest in a Pipeline
