I'm using the AWS Machine Learning regression to predict the waiting time in a line of a restaurant, in a specific weekday/time.
Today I have around 800k data.
Example Data:
restaurantID (rowID)weekDay (categorical)time (categorical)tablePeople (numeric)waitingTime (numeric - target)1 sun 21:29 2 23
2 fri 20:13 4 43
...
I have two questions:
1)
Should I use time as Categorical or Numeric?
It's better to split into two fields: minutes and seconds?
2)
I would like in the same model to get the predictions for all my restaurants.
Example:
I expected to send the rowID identifier and it returns different predictions, based on each restaurant data (ignoring others data).
I tried, but it's returning the same prediction for any rowID. Why?
Should I have a model for each restaurant?
There are several problems with the way you set-up your model
1) Time in the form you have it should never be categorical. Your model treats times 12:29 and 12:30 as two completely independent attributes. So it will never use facts it learn about 12:29 to predict what's going to happen at 12:30. In your case you either should set time to be numeric. Not sure if amazon ML can convert it for you automatically. If not just multiply hour by 60 and add minutes to it. Another interesting thing to do is to bucketize your time, by selecting which half hour or wider interval. You do it by dividing (h*60+m) by some number depending how many buckets you want. So to try 120 to get 2 hr intervals. Generally the more data you have the smaller intervals you can have. The key is to have a lot of samples in each bucket.
2) You should really think about removing restaurantID from your input data. Having it there will cause the model to over-fit on it. So it will not be able to make predictions about restaurant with id:5 based on the facts it learn from restaurants with id:3 or id:9. Having restaurant id there might be okay if you have a lot of data about each restaurant and you don't care about extrapolating your predictions to the restaurants that are not in the training set.
3) You never send restaurantID to predict data about it. The way it usually works you need to pick what are you trying to predict. In your case probably 'waitingTime' is most useful attribute. So you need to send weekDay, time and number of people and the model will output waiting time.
You should think what is relevant for the prediction to be accurate, and you should use your domain expertise to define the features/attributes you need to have in your data.
For example, time of the day, is not just a number. From my limited understanding in restaurant, I would drop the minutes, and only focus on the hours.
I would certainly create a model for each restaurant, as the popularity of the restaurant or the type of food it is serving is having an impact on the wait time. With Amazon ML it is easy to create many models as you can build the model using the SDK, and even schedule retraining of the models using AWS Lambda (that mean automatically).
I'm not sure what the feature called tablePeople means, but a general recommendation is to have as many as possible relevant features, to get better prediction. For example, month or season is probably important as well.
In contrast with some answers to this post, I think resturantID helps and it actually gives valuable information. If you have a significant amount of data per each restaurant then you can train a model per each restaurant and get a good accuracy, but if you don't have enough data then resturantID is very informative.
1) Just imagine what if you had only two columns in your dataset: restaurantID and waitingTime. Then wouldn't you think the restaurantID from the testing data helps you to find a rough waiting time? In the simplest implementation, your waiting time per each restaurantID would be the average of waitingTime. So definitely restaurantID is a valuable information. Now that you have more features in your dataset, you need to check if restaurantID is as effective as the other features or not.
2) If you decide to keep restaurantID then you must use it as a categorical string. It should be a non-parametric feature in your dataset and maybe that's why you did not get a proper result.
On the issue with day and time I agree with other answers and considering that you are building your model for the restaurant, hourly time may give a more accurate result.
Related
While training my predictor I came across this error and I got stuck how to fix it.
I have two data-series, a "Target time-series data" with 9234 rows and a single "item_id" and a second one that is "Related time-series data" with the same number of rows as I only have a single id.
I'm setting de data with a window of 180 days, what is exactly the difference between the second and the first number that has appeared on the error, 9414 - 9234 = 180.
We were unable to train your predictor.
Please ensure there are no missing values for any items in the related time series, All items need data until 2020-03-15 00:00:00.0. For example, following items have missing data: item: brl only has 9234/9414 required datapoints starting 1994-06-07 00:00:00.0, please refer to documentation for additional details.
Once my data don't have missing data and it's on a daily basis why is it returning this error?
My data starts on 1994-06-07 and ends on 2019-09-17. Why should I have 9414 data points rather than 9234?
Should I take out 180 days in my "Target time-series data"?
The future values of the related time-series data must be known.
Example of a good related-time series: You know past and future days in which marketing has or will send email newsletters promoting the product you're forecasting. You can use this data as a related-time series.
Example of a bad related-time series: You notice that Google searches for your brand correlated with the sale of your product. As a result you want to use it as a related-time series. Since you don't know how many searches will occur in the future, so you can't use this as a related time series.
In you case, You have TARGET_TIME_SERIES data for 9414 days and you want to predict demand for the next 180 days. That means your RELATED_TIME_SERIES data should be 9594 days.
Edit: I have not tested this with amazon's forecasting product. I'm basing my answer on working with Facebook Prophet (which is one of the models amazon forcast uses). Please let me know if my solution worked.
I'm importing text items to Google's AutoML. Each row contains around 5000 characters and I'm adding 70K of these rows. This is a multi-label data set. There is no progress bar or indication of how long this process will take. Its been running for a couple of hours. Is there any way to calculate time remaining or total estimated time. I'd like to add additional data sets, but I'm worried that this will be a very long process before the training even begins. Any sort of formula to create even a semi-wild guess would be great.
-Thanks!
I don't think that's possible today, but I filed a feature request [1] that you can follow for updates. I asked for both training and importing data, as for training it could be useful too.
I tried training with 50K records (~ 300 bytes/record) and the load took more than 20 mins after which I killed it. I retried with 1K, which ran for 20 mins and then emailed me an error message saying I had multiple labels per input (yes, so what? training data is going to have some of those) and I had >100 labels. I simplified the classification buckets and re-ran. It took another 20 mins and was successful. Then I ran 'training' which took 3 hours and billed me $11. That maps to $550 for 50K recs, assuming linear behavior. The prediction results were not bad for a first pass, but I got the feeling that it is throwing a super large neural net at the problem. Would help if they said what NN it was and its dimensions. They do say "beta" :)
don't wast your time trying to using google for text classification. I am a GCP hard user but microsoft LUIS is far better, precise and so much faster that I can't believe that both products are trying to solve same problem.
Luis has a much better documentation, support more languages, has a much better test interface, way faster.. I don't know if is cheaper yet because the pricing model is different but we are willing to pay more.
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.
I have a booking system for something where the price can change based on the day. The admins for the site can make these changes. If a booking crosses the boundary of a daily rate, they pay pro-rata for the rates they used.
I'm losing confidence in how this is implemented. There are at least two ways:
Having Rates that specify their validity (start, end fields) and then working out which of those apply. But which overlapping ones take priority? Etc. Nasty. This is what we're trying to do and cannot currently answer sufficiently well.
The same except that there is some form of unique quality to date so that no two rates can overlap. The problem here is we'd need to split existing Rates on insert and rejoin two on delete/edit, etc if they had the same value. We'd need to make sure there were no gaps. It requires some heavy ORM overriding.
Keeping a DayRate table with every day defined. This means keeping a load of extra data around but most bookings are for tens of days, not thousands so I'm not worried about the database bandwidth requirements here. Date would be primary-unique and I'd just do a range filter for grabbing which ones I need to factor in.
The problem is generating these dates ahead of time. I know that as soon as I implement this, somebody will make a booking for 2032. Is there a good way around this or should we limit them?
None of these answers seems great and I have to imagine that I'm not the first guy with a booking system. Is there a better way of keeping track of a rate over a contiguous (possibly infinite) amount of time?
I have a bit of a unique problem here. I currently have two warehouses that I ship items out of for selling on Amazon, my primary warehouse and my secondary warehouse. Shipping out of the secondary warehouse takes significantly longer than shipping from the main warehouse, hence why it is referred to as the "secondary" warehouse.
Some of our inventory is split between the two warehouses. Usually this is not an issue, but we keep having a particular issue. Allow me to explain:
Let's say that I have 10 red cups in the main warehouse, and an additional 300 in the secondary warehouse. Let's also say it's Christmas time, so I have all 310 listed. However, from what I've seen, Amazon only allows one shipping time to be listed for the inventory, so the entire 310 get listed as under the primary warehouse's shipping time (2 days) and doesn't account for the secondary warehouse's ship time, rather than split the way that they should be, 10 at 2 days and 300 at 15 days.
The problem comes in when someone orders an amount that would have to be split across the two warehouses, such as if someone were to order 12 of said red cups. The first 10 would come out of the primary warehouse, and the remaining two would come out of the secondary warehouse. Due to the secondary warehouse's shipping time, the remaining two cups would have to be shipped out at a significantly different date, but Amazon marks the entire order as needing to be shipped within those two days.
For a variety of reasons, it is not practical to keep all of one product in one warehouse, nor is it practical to increase the secondary warehouse's shipping time. Changing the overall shipping date for the product to the longest ship time causes us to lose the buy box for the listing, which really defeats the purpose of us trying to sell it.
So my question is this: is there some way in MWS to indicate that the inventory is split up in terms of shipping times? If so, how?
Any assistance in this matter would be appreciated.
Short answer: No.
There is no way to specify two values for FulfillmentLatency, in the same way as there is no way to specify two values for Quantity in stock. You can only ever have one inventory with them (plus FBA stock)
Longer answer: You could.
Sign up twice with Amazon:
"MySellerName" has an inventory of 10 and a fulfillment latency of 2 days
"MySellerName Overseas Warehouse" has an inventory of 300 and a fulfillment latency of 30 days
I haven't tried by I believe Amazon will then automatically direct the customer to the best seller for them, which should be "MySellerName" for small orders and "MySellerName Overseas Warehouse" for larger quantities.