[Spark ML] [Pyspark] [Scenario Beginner] [Level Beginner]
I am still struggling with getting fit() to work on my dataset.
The Spark ML exception that is the issue is:
LAPACK.dppsv returned 6 because A is not positive definite. Is A derived from a
singular matrix (e.g. collinear column values)?
Comparing my standardized Weight values with the tutorial's values. I see I
have some negative values. The tutorial values are all positive. The above
exception message mentions non positive value, so it's probably my issue.
The calculation for standardizing my Weight values Weight - Weight_Mean /
Weight_StdDev is producing negative values when the Weight which can between 1
- 72000 is small.
I have a suggestion to try using MinMaxScaler. But, it operates on a Vector
and I have a single value. Not sure, I see how I make this work.
My stats is very old. Is there a way to achieve positive values only when
standardizing something like my Weight values above?
Thanks.
-S
From: Steve Pruitt
Sent: Monday, April 01, 2019 12:39 PM
To: user
Subject: [EXTERNAL] - [Spark ML] [Pyspark] [Scenario Beginner] [Level Beginner]
After following a tutorial on Recommender systems using Pyspark / Spark ML. I
decided to jump in with my own dataset. I am specifically trying to predict
video suggestions based on an implicit feature for the time a video was
watched. I wrote a generator to produce my dataset. I have a total of five
videos each 1200 seconds in length. I randomly selected which videos a user
watched and a random time between 0-1200. I generated 10k records. Weight is
the time watched feature. It looks a like this.
UserId,VideoId,Weight
0,1,645
0,2,870
0,3,1075
0,4,486
0,5,900
1,1,353
1,2,988
1,3,152
1,4,953
1,5,641
2,3,12
2,4,444
2,5,87
3,2,658
3,4,270
3,5,530
4,2,722
4,3,255
:
After reading the dataset. I convert all columns to Integer in place.
Describing Weight produces:
summary Weight
0 count 30136
1 mean 597.717945314574
2 stddev 346.475684454489
3 min 0
4 max 1200
Next, I standardized the weight column by:
df = dataset.select(mean('Weight').alias('mean_weight'),
stddev('Weight').alias('stddev_weight')).crossJoin(dataset).withColumn('weight_scaled',
(col('Weight') - col('mean_weight')) / col('stddev_weight'))
df.toPandas().head() shows:
mean_weight stddev_weight UserId VideoId Weight weight_scaled
0 597.717945 346.47568401 6450.136466
1 597.717945 346.47568402 8700.785862
2 597.717945 346.475684031075 1.377534
3 597.717945 346.47568404486
-0.322441
4 597.717945 346.47568405900 0.872448
:
10 597.717945 346.475684 2 3 12 -1.690502
11 597.717945 346.475684 2 4 444-0.443662
12 597.717945 346.475684 2 5 87
-1.474037
:
After splitting df 80 / 20 to get training / testing
I defined the ALS algo with:
als = ALS(maxIter=10, regParam=0.1, userCol='UserId', itemCol='VideoId',
implicitPrefs=True, ratingCol='weight_scaled', coldStartStrategy='drop')
and then
model = als.fit(trainingData)
Calling fit() is where I get the following error, I don't understand.
Py4JJavaError Traceback (most recent call last)
in
> 1 model = als.fit(trainingData)
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\pyspark\ml\base.py in fit(self,
dataset, params)
130 return self.copy(params)._fit(dataset)
131 else:
--> 132 return self._fit(dataset)
133 else:
134 raise ValueError("Params must be either a param map or a
list/tuple of param maps, "
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\pyspark\ml\wrapper.py in
_fit(self, dataset)
286
287 def _fit(self, dataset):
--> 288 java_model = self._fit_java(dataset)
289 model = self._create_model(java_model)
290 return self._copyValues(model)
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\pyspark\ml\wrapper.py in
_fit_java(self, dataset)
283 """
284 self._transfer_params_to_java()
--> 285 return self._java_obj.fit(dataset._jdf)
286
287 def _fit(self, dataset):
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\lib\py4j-0.10.6-src.zip\py4j\java_gateway.py
in __call__(self, *args)
1158 answer = self.gateway_client.send_command(command)
1159 return_value = get_return_value(
-> 1160 answer, self.gateway_client, self.target_id, self.name)
1161
1162 for temp_arg in temp_args:
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\pyspark\sql\utils.py in
deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63
[Spark ML] [Pyspark] [Scenario Beginner] [Level Beginner]
After following a tutorial on Recommender systems using Pyspark / Spark ML. I
decided to jump in with my own dataset. I am specifically trying to predict
video suggestions based on an implicit feature for the time a video was
watched. I wrote a generator to produce my dataset. I have a total of five
videos each 1200 seconds in length. I randomly selected which videos a user
watched and a random time between 0-1200. I generated 10k records. Weight is
the time watched feature. It looks a like this.
UserId,VideoId,Weight
0,1,645
0,2,870
0,3,1075
0,4,486
0,5,900
1,1,353
1,2,988
1,3,152
1,4,953
1,5,641
2,3,12
2,4,444
2,5,87
3,2,658
3,4,270
3,5,530
4,2,722
4,3,255
:
After reading the dataset. I convert all columns to Integer in place.
Describing Weight produces:
summary Weight
0 count 30136
1 mean 597.717945314574
2 stddev 346.475684454489
3 min 0
4 max 1200
Next, I standardized the weight column by:
df = dataset.select(mean('Weight').alias('mean_weight'),
stddev('Weight').alias('stddev_weight')).crossJoin(dataset).withColumn('weight_scaled',
(col('Weight') - col('mean_weight')) / col('stddev_weight'))
df.toPandas().head() shows:
mean_weight stddev_weight UserId VideoId Weight weight_scaled
0 597.717945 346.47568401 6450.136466
1 597.717945 346.47568402 8700.785862
2 597.717945 346.475684031075 1.377534
3 597.717945 346.47568404486
-0.322441
4 597.717945 346.47568405900 0.872448
:
10 597.717945 346.475684 2 3 12 -1.690502
11 597.717945 346.475684 2 4 444-0.443662
12 597.717945 346.475684 2 5 87
-1.474037
:
After splitting df 80 / 20 to get training / testing
I defined the ALS algo with:
als = ALS(maxIter=10, regParam=0.1, userCol='UserId', itemCol='VideoId',
implicitPrefs=True, ratingCol='weight_scaled', coldStartStrategy='drop')
and then
model = als.fit(trainingData)
Calling fit() is where I get the following error, I don't understand.
Py4JJavaError Traceback (most recent call last)
in
> 1 model = als.fit(trainingData)
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\pyspark\ml\base.py in fit(self,
dataset, params)
130 return self.copy(params)._fit(dataset)
131 else:
--> 132 return self._fit(dataset)
133 else:
134 raise ValueError("Params must be either a param map or a
list/tuple of param maps, "
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\pyspark\ml\wrapper.py in
_fit(self, dataset)
286
287 def _fit(self, dataset):
--> 288 java_model = self._fit_java(dataset)
289 model = self._create_model(java_model)
290 return self._copyValues(model)
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\pyspark\ml\wrapper.py in
_fit_java(self, dataset)
283 """
284 self._transfer_params_to_java()
--> 285 return self._java_obj.fit(dataset._jdf)
286
287 def _fit(self, dataset):
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\lib\py4j-0.10.6-src.zip\py4j\java_gateway.py
in __call__(self, *args)
1158 answer = self.gateway_client.send_command(command)
1159 return_value = get_return_value(
-> 1160 answer, self.gateway_client, self.target_id, self.name)
1161
1162 for temp_arg in temp_args:
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\pyspark\sql\utils.py in
deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
C:\Executables\spark-2.3.0-bin-hadoop2.7\python\lib\py4j-0.10.6-src.zip\py4j\protocol.py
in get_return_value(answer, gateway_client, target_id, name)
318 raise Py4JJavaError(
319 "An error occurred while calling {0}{1}{2}.\n".
--> 320 format(target_id, ".", name), value)
321 else:
322 raise Py4JError(
Py4JJavaError: An error occurred while calling o211.fit.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 5 in
stage 61.0 failed 1 times, most recent failure: Lost task 5.0 in stage 61.0
(TID 179, localhost, executor driver):
org.apache.spark.ml.optim.SingularMatrixException: LAPACK.dppsv returned 6
because A is not positive definite. Is A derived from a singular matrix (e.g.
collinear column values)?
at
org.apache.spark.mllib.linalg.CholeskyDecomposition$.checkReturnValue(CholeskyDecomposition.scala:65)
at
