I tried to search and experimented, but couldn't figure out, how to add slim to rails stats views statistics. It is counting only .erb templates, but I want .slim to be added as these are views too.
% bin/rails stats
+----------------------+--------+--------+---------+---------+-----+-------+
| Name | Lines | LOC | Classes | Methods | M/C | LOC/M |
+----------------------+--------+--------+---------+---------+-----+-------+
| Controllers | 3245 | 1634 | 57 | 218 | 3 | 5 |
| Helpers | 186 | 149 | 0 | 18 | 0 | 6 |
| Jobs | 34 | 20 | 2 | 2 | 1 | 8 |
| Models | 879 | 541 | 25 | 77 | 3 | 5 |
| Mailers | 85 | 53 | 3 | 6 | 2 | 6 |
| Channels | 46 | 28 | 3 | 4 | 1 | 5 |
| Views | 0 | 0 | 0 | 0 | 0 | 0 |
+----------------------+--------+--------+---------+---------+-----+-------+
I could add an extra rules for something like "Slim views", but this would count the .erb templates in views too.
The below query takes over 1 second & consumer about 7 MB when cardinality b/w users to posts is about 8000 (one user views about 8000 posts). It is difficult to scale this due to high & linearly growing latencies & memory consumption. Is there a possibility to model this differently and/or optimise the query?
Query
PROFILE MATCH (u:User)-[:CREATED]->(p:Post) WHERE NOT (:User{ID: 2})-[:VIEWED]->(p) RETURN p.ID
Plan
| Plan | Statement | Version | Planner | Runtime | Time | DbHits | Rows | Memory (Bytes) |
+-----------------------------------------------------------------------------------------------------------+
| "PROFILE" | "READ_ONLY" | "CYPHER 4.1" | "COST" | "INTERPRETED" | 1033 | 3721750 | 10 | 6696240 |
+-----------------------------------------------------------------------------------------------------------+
+------------------------------+-----------------------------------------------+----------------+------+---------+-----------+----------------+----------------+
| Operator | Details | Estimated Rows | Rows | DB Hits | Cache H/M | Memory (Bytes) | Ordered by |
+------------------------------+-----------------------------------------------+----------------+------+---------+-----------+----------------+----------------+
| +ProduceResults#neo4j | `p.ID` | 2158 | 10 | 0 | 0/0 | | |
| | +-----------------------------------------------+----------------+------+---------+-----------+----------------+----------------+
| +Projection#neo4j | p.ID AS `p.ID` | 2158 | 10 | 10 | 0/0 | | |
| | +-----------------------------------------------+----------------+------+---------+-----------+----------------+----------------+
| +Filter#neo4j | u:User | 2158 | 10 | 10 | 0/0 | | |
| | +-----------------------------------------------+----------------+------+---------+-----------+----------------+----------------+
| +Expand(All)#neo4j | (p)<-[anon_15:CREATED]-(u) | 2158 | 10 | 20 | 0/0 | | |
| | +-----------------------------------------------+----------------+------+---------+-----------+----------------+----------------+
| +AntiSemiApply#neo4j | | 2158 | 10 | 0 | 0/0 | | |
| |\ +-----------------------------------------------+----------------+------+---------+-----------+----------------+----------------+
| | +Expand(Into)#neo4j | (anon_47)-[anon_61:VIEWED]->(p) | 233 | 0 | 3695819 | 0/0 | 6696240 | anon_47.ID ASC |
| | | +-----------------------------------------------+----------------+------+---------+-----------+----------------+----------------+
| | +NodeUniqueIndexSeek#neo4j | UNIQUE anon_47:User(ID) WHERE ID = $autoint_0 | 8630 | 8630 | 17260 | 0/0 | | anon_47.ID ASC |
| | +-----------------------------------------------+----------------+------+---------+-----------+----------------+----------------+
| +NodeByLabelScan#neo4j | p:Post | 8630 | 8630 | 8631 | 0/0 | | |
+------------------------------+-----------------------------------------------+----------------+------+---------+-----------+----------------+----------------+
Yes, this can be improved.
First, let's understand what this is doing.
First, it starts with a NodeByLabelScan. That makes sense, there's no avoiding that.
But then, for every node of the label (the following executes PER ROW!), it matches to user 2, and expands all :VIEWED relationships from user 2 to see if any of them is the post for that particular row.
Can you see why this is inefficient? There are 8630 post nodes according to the PROFILE plan, so user 2 is looked up by index 8630 times, and their :VIEWED relationships are expanded 8630 times. Why 8630 times? Because this is happening per :Post node.
Instead, try this:
MATCH (:User{ID: 2})-[:VIEWED]->(viewedPost)
WITH collect(viewedPost) as viewedPosts
MATCH (:User)-[:CREATED]->(p:Post)
WHERE NOT p IN viewedPosts
RETURN p.ID
This changes things up a bit.
First it matches to user 2's viewed posts (the lookup and expansion is performed only once), then those viewed posts are collected.
Then it will do a label scan, and filter such that the post isn't in the collection of viewed posts.
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 2 years ago.
Improve this question
Problem:
I have a dataset about hedge fund. It contains monthly hedge fund returns and some financial metrics. I calculated metrics for every month from 2010 to 2019 December. (2889 monthly data) I want to binary classification and predict hedge funds' class basis on these metrics for next month. I want make prediction for T+1 from T time. And i want use random forest and other classifiers(Decision Tree,KNN,SVM,logistic regression). I know this dataset is time series problem, how do i convert this to machine learning problem.
I am open to your suggestions and advisories as to what method or approach should be followed in modeling, feature engineering and editing this data set.
Additional Questions:
1)How do I make a data split when using this data for training and test ? 0,80-0,20?. Is there any other method of validation you can recommend?
2)some funds are added to the data later, so not all funds have data of equal length, for example, the "AEB" fund established in 2015 has no data before 2015. There are a few such funds, do they cause problems, or is it better to delete them and remove them from the dataset? I have a total of 27 different fund data.
3)In addition, I have changed the tickers/names of the hedge funds to numeric ID, is it possible to do dummy encoding, would it be better for performance?
Sample Dataset:
Date | Fund Name / Ticker | sharpe | sortino | beta | alpha | target |
------------|--------------------|--------|---------|-------|-------|--------|--
31.03.2010 | ABC | -0,08 | 0,025 | 0,6 | 0,13 | 1 |
31.03.2010 | DEF | 0,41 | 1,2 | 1,09 | 0,045 | 0 |
31.03.2010 | SDF | 0,03 | 0,13 | 0,99 | -0,07 | 1 |
31.03.2010 | CBD | 0,71 | -0,05 | 1,21 | 0,2 | 1 |
30.04.2010 | ABC | 0,05 | -0,07 | 0,41 | 0,04 | 0 |
30.04.2010 | DEF | 0,96 | 0,2 | 1,09 | 1,5 | 0 |
30.04.2010 | SDF | -0,06 | 0,23 | 0,13 | 0,23 | 0 |
30.04.2010 | CBD | 0,75 | -0,01 | 0,97 | -0,06 | 1 |
: | : | : | : | : | : | : |
: | : | : | : | : | : | : |
30.12.2019 | ABC | 0,05 | -0,07 | 0,41 | 0,04 | 1 |
30.12.2019 | DEF | 0,96 | 0,2 | 1,09 | 1,5 | 0 |
30.12.2019 | SDF | -0,06 | 0,23 | 0,13 | 0,23 | 0 |
30.12.2019 | CBD | 0,75 | -0,01 | 0,97 | -0,06 | 1 |
30.12.2019 | FGF | 1,45 | 0,98 | -0,03 | 0,55 | 1 |
30.12.2019 | AEB | 0,25 | 1,22 | 0,17 | -0,44 | 0 |
My Idea and First Try:
I modeled one example. I used a method like this, I shifted(-1) back the target variable. So each line was shown the class in which the fund was located in the following month.I did it because of this, I want to predict the next month before that month starts. Predict to T+1 from T.But this model gave a very poor result.(%43)
view of this model dataset:
Date | Fund Name / Ticker | sharpe | sortino | beta | alpha | target |
------------|--------------------|--------|---------|-------|-------|--------|--
31.03.2010 | ABC | -0,08 | 0,025 | 0,6 | 0,13 | 1 |
31.03.2010 | DEF | 0,41 | 1,2 | 1,09 | 0,045 | 0 |
31.03.2010 | SDF | 0,03 | 0,13 | 0,99 | -0,07 | 1 |
31.03.2010 | CBD | 0,71 | -0,05 | 1,21 | 0,2 | 1 |
30.04.2010 | ABC | 0,05 | -0,07 | 0,41 | 0,04 | 0 |
30.04.2010 | DEF | 0,96 | 0,2 | 1,09 | 1,5 | 0 |
30.04.2010 | SDF | -0,06 | 0,23 | 0,13 | 0,23 | 0 |
30.04.2010 | CBD | 0,75 | -0,01 | 0,97 | -0,06 | 1 |
: | : | : | : | : | : | : |
: | : | : | : | : | : | : |
30.12.2019 | ABC | 0,05 | -0,07 | 0,41 | 0,04 | 0 |
30.12.2019 | DEF | 0,96 | 0,2 | 1,09 | 1,5 | 0 |
30.12.2019 | SDF | -0,06 | 0,23 | 0,13 | 0,23 | 1 |
30.12.2019 | CBD | 0,75 | -0,01 | 0,97 | -0,06 | 1 |
30.12.2019 | FGF | 1,45 | 0,98 | -0,03 | 0,55 | 0 |
30.12.2019 | AEB | 0,25 | 1,22 | 0,17 | -0,44 | ? |
There are many approaches out there that you can find. Time series are challenging and its okay to have poor results at the beginning. I advise you do the following:
Add some lags as additional columns in your dataset. You want to predict t+1 and you have t, so try to also compute t-1,t-2, t-3, etc.
In order to know the best number of t-x that you can have, try to do ACF and PACF plots and see the first lags that appear in the in the shaded region
Lags might boost your accuracy
try to normalize/standardize your data when modeling
Try to see if your time series is a random walk, if it is, there are many recent papers that try to tackle the problem of random walk prediction
If your dataset is big enough, try to use some neural networks like LSTM, RNN, GANs, etc that might be better than the shallow models you have mentioned
I really advise you to see the tutorials of Jason Brownlee on Time Series here Jason is super intelligent and you can always add comments to his tutorials. He is also responsive!!
I currently have two datasets, RTWANEW2016.sav and MERGED.sav.
RTWANEW2016:
+----+------------+--------+--------+--------+--------+--------+--------+
| id | date | value1 | value2 | value3 | value4 | value5 | value6 |
+----+------------+--------+--------+--------+--------+--------+--------+
| 1 | 01-03-2006 | 3 | 9 | 85 | 766 | 3 | 45 |
| 1 | 03-23-2010 | 56 | 34 | 23 | 33 | 556 | 43 |
| 2 | 12-04-2014 | 56 | 655 | 523 | 566 | 9 | 9 |
| 3 | 07-23-2011 | 34 | 56 | 661 | 23 | 22 | 11 |
| 4 | 03-05-2007 | 45 | 345 | 222 | 556 | 4566 | 4 |
+----+------------+--------+--------+--------+--------+--------+--------+
MERGED:
+----+------------+--------+--------+--------+
| id | date | value4 | value5 | value6 |
+----+------------+--------+--------+--------+
| 1 | 01-03-2006 | 345 | 44 | 5345 |
| 2 | 12-04-2014 | 522 | 55 | 5444 |
| 4 | 03-05-2007 | 234 | 88 | 9001 |
+----+------------+--------+--------+--------+
I want to update RTWANEW2016 with the values from variables "value4", "value5" and "value6" from MERGED.
Notice that some data RTWANEW2016 has duplicate ID's, but different dates, so I would need to sort by both id and date
See the UPDATE command which is designed to achieve this.
Overview (UPDATE command)
UPDATE replaces values in a master file with updated values recorded
in one or more files called transaction files. Cases in the master
file and transaction file are matched according to a key variable.
The master file and the transaction files must be IBM® SPSS®
Statistics data files or datasets available in the current session,
including the active dataset. UPDATE replaces values and creates a new
active dataset, which replaces the original active dataset.
UPDATE is designed to update values of existing variables for existing
cases. Use MATCH FILES to add new variables to a data file and ADD
FILES to add new cases.
UPDATE FILE='/RTWANEW2016.sav'
/FILE='/MERGED.sav'
/BY=ID Date.
I'm trying to read a text file in Apache Pig Latin that has non-delimited ascii comprising each row. That is, each column in that row begins and ends at a specific position in the row.
Sample definition:
+--------+----------------+--------------+
| Column | Start Position | End Position |
+--------+----------------+--------------+
| A | 1 | 6 |
+--------+----------------+--------------+
| B | 8 | 11 |
+--------+----------------+--------------+
| C | 13 | 15 |
+--------+----------------+--------------+
Sample Data:
+---+---+---+---+---+---+---+----+---+----+----+----+----+----+----+
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
+---+---+---+---+---+---+---+----+---+----+----+----+----+----+----+
| s | a | m | p | l | e | | d | a | t | a | | | h | i |
+---+---+---+---+---+---+---+----+---+----+----+----+----+----+----+
| d | u | d | e | | | | hi | | | | | b | r | o |
+---+---+---+---+---+---+---+----+---+----+----+----+----+----+----+
Expected Output:
sample, data, hi
dude, hi, bro
How do I read this in Pig? PigStorage doesn't seem flexible enough to allow positional delimiting, only string delimiting (comma, tab, etc..).
Looks like Apache provides a loader for this specific use case:
LOAD 'data.txt' USING org.apache.pig.piggybank.storage.FixedWidthLoader('1-6, 8-11, 13-15', 'SKIP_HEADER') AS (a, b, c);
https://pig.apache.org/docs/r0.16.0/api/