I'm using the Bing Ads Python SDK and I'm trying to get an ad report showing the cost data for each ad for each day.
I can successfully query the AdPerformanceReportRequest service and get all the ads and their metrics back.
But I'm getting back all ads with any activity on them, including impressions. I only want rows where cost > 0.
The filter objects are specific to the report type, not to the metric and none of the examples in the SDK Github repo show how to do this.
The Google Ads API lets us specify a where clause in our report query (Where cost > 0). How can I do this with the Bing Ads API?
Do I have to download all the data and filter myself (this report is quite large, about 100Mb, and about 75% smaller when filtered)? Or is there a more efficient way to do this by not requesting those rows that are useless to me at all?
Any point in the right direction would be very much appreciated.
Edit: As was pointed out below, this is not currently possible. A tip if you're trying to filter yourself locally is that the metrics in the in-memory report object record is a string. So if you want to filter out zero values, you need to filter out values that are string representations of '0.00', not '0' or the integer value 0.
The reporting service via Bing Ads API does not support such a filter. I suggest please vote on this related feature request and append comments / details about your scenario as needed. Thank you.
Related
I am currently using Microsoft Academic's datadump for a project and unable to identify the total number of theses & dissertations(T&D) present. Based on their website, 38% of data is categorised to OTHERS type (one among them is T&D). But their 60+GB CSV dump doesn't explicitly indicate the T&D records. Can someone help me with the statistics for T&D or how find the same?
I tried their API too and unable to find using their API too.
The Microsoft Academic Graph does not explicitly segment publication sub-types by thesis or dissertations, which means that neither the API or website will either.
If this is something you'd like to see added, please let them know by using the "Feedback" link in the lower-right corner on the Microsoft Academic website.
I am building a package that uses the Google Analytics API for Python.
But, in severous cases when I have multiple dimensions the extraction by day is sampled.
I know that if I use sampling_level = LARGE will use a sample more accurate.
But, somebody knows if has a way to reduce a request that you can extract one day without sampling?
Grateful
setting sampling to LARGE is the only method we have to decide the amount of sampling but as you already know this doesn't prevent it.
The only way to reduce the chances of sampling is to request less data. A reduced number of dimensions and metrics as well as a shorter date range are the best ways to ensure that you dont get sampled data
This is probably not the answer you want to hear but, one way of getting unsampled data from Google analytics is to use unsampled reports. However this requires that you sign up for Google Marketing Platform. With these you can create an unsampled report request using the API or the UI.
There is also a way to export the data to Big Query. But you lose the analysis that Google provides and will have to do that yourself. This too requires that you sign up for Google Marketing Platform.
there are several tactics of building unsampled reports, most popular is splitting your report into shorter time ranges up to hours. Mark Edmondson did a great work on anti-sampling in his R package so you might find it useful. You may start with this blog post https://code.markedmondson.me/anti-sampling-google-analytics-api/
I want to use Azure ML to find related products using information from receipts from a store.
I got a file of reciepts:
44366,136778
79619,88975
78861,78864
53395,78129,78786,79295,79353,79406,79408,79417,85829,136712
32340,33973
31897,32905
32476,32697,33202,33344,33879,34237,34422,48175,55486,55490,55498
17800
32476,32697,33202,33344,33879,34237,34422,48175,55490,55497,55498,55503
47098
136974
85832
Each row represent one receipt and each number is a product id.
Given a product id I want to get a list of similar products, i.e. products that was bought together by other customers.
Can anyone point me in the right direction of how do to this?
This seems a good fit for their frequently bought together service (https://datamarket.azure.com/dataset/amla/mba). You may have to preprocess the dataset to get it in the required format. This service has a web UI as well: https://marketbasket.cloudapp.net/
This is a typical problem for Recommender, you can use a model called Machbox recommender to cover such a problem.
Recommender typically use Scoring about items to propose and the use some tricky calculation to predict scores for items users had not scored yet ( a score would be typically 1 user bought the item, 0 he did not)
If you need more details let me know ..(you have access to a free version of Azure ML where you can try all this)
Regards
I am looking into using ElasticSearch as a search engine for one of the projects I am working on.
There is still one thing which I need to find an answer for, and I hope someone inhere can help.
The customer want to be able to see some search statistic, like google analytics. Most searched words, new search words and so on.
Is there a way to easily setup this type of search statistic. My idea is something like ElasticSearch stores search history, about the search request made to the REST API. Then my customer can use Kibana or some other visual tool to monitor the search history of ElasticSearch.
Hope someone can help me with an answer for this.
Regards
Jacob
You could adjust the slow log to a time which it will capture all requests, however this will then produce large log files which will require maintenance. You could write an application which handles all of your ES requests, takes the search phrase and indexes this in a separate index i.e. your search history index and then deals with the actual request as normal, returning the response to the user.
I'm trying to organize a solr search engine. I've already set up the misspelling system and the suggestions.
However I can't seem to find how to retrieve the top 10 most searched words/terms/keywords in solr/lucene. How can I get this? I want to display those on my homepage.
Solr does not provide this kind of feature out of the box. There is the StatsComponent, that provides you with all kind of statistics, but all of those are numeric only.
Depending on how you access solr (directly or via your own app) you could intercept all calls an log the query string. I did this in a recent project where I logged a queries to a database. If you submit all keywords to an other core on your solr server, you can faceting queries on your search terms as described by Hyque
You could use a facet for retrieving the Top X words like this:
http://yourservergoeshere/solr/select?q=*&wt=xml&indent=true&facet=true&facet.query=*&facet.field=message&facet.limit=10&facet.minCount=1
The value of facet.field depends on the field you like to search in. With facet.limit you'll (obviously) limit the amount of results to 10. You'll find the facet results at the end of the results, starting with "facet_counts"
Edit: I really should go to bed earlier. I didn't see the "most searched" in your question. Sorry for that.
Apache Solr does not provide any such capability as of today. There is a desire for this and a JIRA ticket corresponding to it. You can vote for it if you'd like to see it in Solr some day: https://issues.apache.org/jira/browse/SOLR-10359.
The stats component provides information around statistics, but it's mostly numeric in nature. You could parse server logs and come up with a way to build a Frequently Searched Terms (e.g. pump those logs in SiLK or Kibana for visualization).
If you have the ability to change the front end and add some javascript code to the UI or can intercept the search request and make an async or batch calls to APIs for tracking, you can use SearchStax Analytics that provides Search Analytics that tracks searches, clicks, cart actions, revenue, etc.