Trophies are achievements that can be awarded to users based on some aspect of a review request. I want to know what are those aspects.
Please see this blog post for an explanation:
Generally big, round, numbers (1000, 2000, etc.) sometimes known as Odometer numbers.
Palindromes (e.g 416614) trigger the Fish Trophy as well.
Related
I decided to post here a kind information for support I put in Statalist yesterday. I have not yet received a possible hint and thought it could be useful to extend the audience by posting it here.
The link to the original post is the following:
https://www.statalist.org/forums/forum/general-stata-discussion/general/1659627-choose-the-appropriate-way-to-deal-with-weights-in-svyset?view=thread
Dear Members,
I defined a questionnaire to gather respondents' willingness to get vaccinated against COVID-19 via a discrete choice experiment. I relied on a company specialized in political opinion polls and market research to administer the survey. The company computed a weight for each respondent based on 1) the geographical location where the respondent lives (five macroareas of Italy), 2) whether the respondent has a bachelor degree or not, and 3) to which age group she/he pertains (five classes are considered).
The sum of the weights is equal to the number of individuals in the database. The individuals pertaining to the age classes 30-39 and 40-49 are oversampled, as per our request (related to a research hypothesis). The proportion of such two classes within the sample is larger than the actual in the Italian population. Weights are computed in order to take into account for this feature and guarantee that the sample is representative of the characteristics of the Italian population.
I will use the data to estimate a logit model, multinomial logit models and mixed logit models.
The issue I am facing with is the proper path to follow to declare the nature of the weight. I have no experience in the use of Stata to deal with this issue.
I am using Stata 17 on a PC with Windows 10 Pro 64 bit.
Combining the information from the video, the svysvyset manual and the results from the help for "weight" I tried to think what is the most appropriate solution.
I tried to add here the code multiple times as well but I kept receiving an error message on how I formatted it. My apologies
I use Google Cloud Speech Transcription as following :
video_client = videointelligence.VideoIntelligenceServiceClient()
features = [videointelligence.enums.Feature.SPEECH_TRANSCRIPTION]
operation = video_client.annotate_video(gs_video_path, features=features)
result = operation.result(timeout=3600)
And I present the transcript and store the transcript in Django Objects using PostgreSQL as following :
transcriptions = response.annotation_results[0].speech_transcriptions
for transcription in transcriptions:
best_alternative = transcription.alternatives[0]
confidence = best_alternative.confidence
transcript = best_alternative.transcript
if SpeechTranscript.objects.filter(text = transcript).count() == 0:
SpeechTranscript.objects.create(text = transcript,
confidence = confidence)
print(f"Adding -> {confidence:4.10%} | {transcript.strip()}")
else:
pass
For instance the following is the text that I receive from a sample video :
94.9425220490% | I refuse to where is it short sleeve dress shirt. I'm just not going there the president of the United States is a visit to Walter Reed hospital in mid-july format was the combination of weeks of cajoling by trump staff and allies to get the presents for both public health and political perspective wearing a mask to protect against the spread of covid-19 reported in advance of that watery trip and I quote one presidential aide to the president to set an example for a supporters by wearing a mask and the visit.
94.3865835667% | Mask wearing is because well science our best way to slow the spread of the coronavirus. Yes trump or Matthew or 3 but if you know what he said while doing sell it still anybody's guess about what can you really think about NASCAR here is what probably have a mass give you probably have a hospital especially and that particular setting were you talking to a lot of people I think it's but I do believe it. Have a a time and a place very special line trump saying I've never been against masks but I do believe they have a time and a place that isn't exactly a ringing endorsement for mask wearing.
94.8513686657% | Republican skip this isn't it up to four men over the perfumer's that wine about time and place should be a blinking red warning light for people who think debate over whether last for you for next coronavirus. They are is finally behind us time in a place lined everything you need to know about weird Trump is like headed next time he'll get watery because it was a hospital and will continue to express not so scepticism to wear masks in public house new CDC guidelines recommending that mask to be worn inside and one social this thing is it possible outside he sent this?
92.9862976074% | He wearing a face mask as agreed presidents prime minister's dictators Kings Queens and somehow. I don't see it for myself literally main door he responded this way back backstage, but they said you didn't need it trump went to Michigan to this later and he appeared in which personality approaching Mark former vice president Joe Biden
94.6677267551% | In his microwave fighting for wearing a mask and he walked onto the stage where it is massive mask there's nobody understands and there's any takes it off you like to have it hanging off you. I think it makes them feel good frankly if you want to know the truth who's got the largest basket together. Seen it because trump thinks that maths make him and people generally I guess what a week or something is resistant wearing one in public from 1 today which has had a correlation between the erosion of the public's confidence and trump have the corner coronavirus and his number is SE6 a second term in the 67.
94.9921131134% | The coronavirus pandemic in the heels of national and swings they both lots of them that show trump slipping further and further behind former vice president Joe Biden when it comes to General Election good policy would seem to make for good politics at all virtually every infectious disease expert believes that wearing masks in public is our best to contain the spread of coronavirus until a vaccine would do well to listen to buy on this one a mare is the point we make episode every Tuesday and Thursday make sure to check them all out.
What is the predicted size of a transcript that is generated within the speech transcription results. What decides the size of each transcript ? What is the max and minimum character length ? How should I design my SQL table column size, in order to be prepared for the expected transcript size ?
As I mentioned in the comments, the Video Intelligence transcripts are splits with roughly 50-60 seconds from the video.
I have created a Public Issue Tracker case, link, so the product team can clarify this information within the documentation. Although, I do not have an eta for this request, I encourage you to follow the case's thread.
I am trying to predict match winner based on the historical data set as shown below,
The data set comprises of IPL seasons and Team_Name_id vs Opponent Team are the team names in IPL. I have set the match id as Row id and created the model. When running realtime testing, the result is not as expected (shown below)
Target is set as Match_winner_id.
Am I missing any configurations? Please help
The model is working perfectly correctly. There's just two problems:
Your input data is not very good
There's no way for the model to know that only one of those two teams should win
Data Quality
A predictive model needs good quality input data on which to reverse-engineer a model that explains a given result. This input data should contain information that can be used to predict a result given a different set of input data.
For example, when predicting house prices, it would need to know the suburb (category), number of bedrooms/bathrooms/parking spaces, age of the building and selling price. It could then predict the selling price for other houses with a slightly different mix of variables.
However, based on your screenshot, you are giving the following information (and probably more) on which to make your prediction:
Teams: Not great, because you are separating Column C and Column D. The model will assume they are unrelated information. It doesn't realise that those two values could be swapped.
Match date: Useless information unless the outcome varies in proportion to time (eg a team continually gets better)
Season: As with Match Date, this is probably useless because you're always predicting the future -- you won't be predicting for a past season
Venue: Only relevant if a particular team always wins at a given venue
Toss Decision: Would this really influence the outcome? Also, it's only known once the game begins, so not great for predicting a future game.
Win Type: You won't know the win type until a game is over, so it's not suitable for predicting a future game.
Score: Again, not known until the actual game, so no good for future predictions.
Man of the Match: Not known for future games.
Umpire: How does an umpire influence the result of a game?
City: Yes, given that home teams often have an advantage.
You have provided very little information that could be used to predict a future game. There is really only the teams and the venue. Everything else is either part of the game itself or irrelevant.
Picking only one of the two teams
When the ML model looks at your data and tries to make a prediction, it will look at all the data you have provided. For example, it might notice that for a given venue and season, Team 8 has a higher propensity to win. Therefore, given that venue and season, it will favour a win by Team 8. The model has no concept that the only possible outcome is one of the two teams given in columns C and D.
You are predicting for two given teams and you are listing the teams in either Column C or Column D and this makes no sense -- the result is the same if you swapped the teams between columns, but the model has no concept of this. Also, information about Team 1 vs Team 2 is totally irrelevant for Team 3 vs Team 4.
What you should do is create one dataset per team, listing all their matches, plus a column that shows the outcome -- either a boolean (Win/Lose) or a value that represents the number of runs by which they won (where negative is a loss). You would then ask them model to predict the result for that team, given the input data, which would be win/lose or a points above/below the other team.
But at the core, I think that your input data doesn't have enough rich content to be able to make a sensible prediction. Just ask yourself: "What data would I like to know if I were to guess which team would win?" It would probably be past results, weather conditions, which players were on each team, how many matches they played in the last week, etc. None of this information is being provided as input on each line of your input data.
Having implemented an algorithm to recommend products with some success, I'm now looking at ways to calculate the initial input data for this algorithm.
My objective is to calculate a score for each product that a user has some sort of history with.
The data I am currently collecting:
User order history
Product pageview history for both anonymous and registered users
All of this data is timestamped.
What I'm looking for
There are a couple of things I'm looking for suggestions on, and ideally this question should be treated more for discussion rather than aiming for a single 'right' answer.
Any additional data I can collect for a user that can directly imply an interest in a product
Algorithms/equations for turning this data into scores for each product
What I'm NOT looking for
Just to avoid this question being derailed with the wrong kind of answers, here is what I'm doing once I have this data for each user:
Generating a number of user clusters (21 at the moment) using the k-means clustering algorithm, using the pearsons coefficient for the distance score
For each user (on demand) calculating their a graph of similar users by looking for their most and least similar users within their cluster, and repeating for an arbitrary depth.
Calculating a score for each product based on the preferences of other users within the user's graph
Sorting the scores to return a list of recommendations
Basically, I'm not looking for ideas on what to do once I have the input data (I may need further help with that later, but it's not the point of this question), just for ideas on how to generate this input data in the first place
Here's a haymaker of a response:
time spent looking at a product
semantic interpretation of comments left about the product
make a discussion page about a product, brand, or product category and semantically interpret the comments
if they Shared a product page (email, del.icio.us, etc.)
browser (mobile might make them spend less time on the page vis-à-vis laptop while indicating great interest) and connection speed (affects amt. of time spent on the page)
facebook profile similarity
heatmap data (e.g. à la kissmetrics)
What kind of products are you selling? That might help us answer you better. (Since this is an old question, I am addressing both #Andrew Ingram and anyone else who has the same question and found this thread through search.)
You can allow users to explicitly state their preferences, the way netflix allows users to assign stars.
You can assign a positive numeric value for all the stuff they bought, since you say you do have their purchase history. Assign zero for stuff they didn't buy
You could do some sort of weighted value for stuff they bought, adjusted for what's popular. (if nearly everybody bought a product, it doesn't tell you much about a person that they also bought it) See "term frequency–inverse document frequency"
You could also assign some lesser numeric value for items that users looked at but did not buy.
Is there a web service or a tool to detect if what a certain text is the name or a person, a place or an object (device).
eg:
Input: Bill Clinton Output: Person
Input: Blackberry Output: Device
Input: New york Output: Place
Accuracy can be low. I have looked at opencyc but I couldnt get it to work. Is there a way I can use WikiPedia for this?
For a start separating a person or a thing will be great.
I think wikipedia would be a very good source. Given the input, you could try and find an entry in wikipedia and scrape the resulting page (if it exists).
Persons and Places should have fairly distinct sets of data - birthdates, locations, etc in the article that you could use to tell them apart, and anything else is an object.
It's worth a shot anyway.
Looking at the output of Wolfram Alpha, it seems that you can possibly identify a person by searching Bill Clinton Birthday or just Bill Clinton, or you can identify a location by searching New York GPS coordinates or just New York, for even better results. Blackberry seems like a tough word for Alpha, because it keeps wanting to interpret it as a fruit. You might have luck searching Froogle to identify a device.
It seems like WA will give you a fairly decent accuracy, at least if you're using famous people/places.
How about using a search engine? Google would be good, and I think Yahoo! has tools for building your own search.
I googled:
Results 1 - 10 of about 27,100,000 for "bill clinton" person
Results 1 - 10 of about 6,050,000 for "bill clinton" place
Results 1 - 10 of about 601,000 for "bill clinton" device
He's a person!
Results 1 - 10 of about 391,000,000 for "new york" place.
Results 1 - 10 of about 280,000,000 for "new york" person.
Results 1 - 10 of about 84,100,000 for "new york" device.
It's a place!
Results 1 - 10 of about 11,000,000 for "blackberry" person
Results 1 - 10 of about 36,600,000 for "blackberry" place
Results 1 - 10 of about 28,000,000 for "blackberry" device
Unfortunately, blackberry is a place as well. :-/
Note that only in the case of 'blackberry' did "device" even get close. Maybe you need to weight the page hit values. What is your application? Do you have any idea which "devices" you'd have to classify? What is the possible range of inputs?
Maybe you want to combine the results you get from different sources.
I think the basic task you're trying to accomplish is more formally known as named entity recognition. This task is nontrivial, and by only inputting the name stripped of any context, you're making it even harder.
For example, we'd like to think examples such as "Bill Clinton" and "New York" are obviously unambiguous, but looking at their disambiguation pages in Wikipedia shows that there are several potential entities they may refer to. "New York" is both a state, city, and movie title. "Bill Clinton" is a bit less ambiguous if you're only looking at Wikipedia, but I'm sure you'll find dozens of Bill Clintons in any phonebook. It might also be the name of someone's sailboat or pet dog. What if someone inputs "Washington"? That could be both a U.S. President, state, district, city, lake, street, island, movie, one of several U.S. navy ships, bridge, as well as other things. Determining which is the "correct" usage you'd want the webservice to return could become very complicated.
As much as Cyc knows, I think you'll find it's still not as comprehensive as Wikipedia. However, the main downside to Wikipedia is that it's essentially unstructured. Personally, I find Cyc's API so convoluted and poorly documented, that parsing Wikipedia's natural language almost seems easier.
If I had to implement such a webservice from scratch, I'd start by downloading a snapshot of Wikipedia, and then writing a parser that would read through all the articles, and generate a named entity index based on article titles. You could manually "classify" a few dozen examples as person/place/object, and train a classifier (Bayesian,Maxent,SVM) to automatically classify other examples based on the word frequencies of their articles.