I want to create a Bayes model in scikit-learn to predict box office openings for movies.
I'm starting with scikit learn and I found many examples on how to load CSV and other table data, but I haven't found examples on how to load attributes with a collection of values, e.g:
Movie 1: Actors: [Actor 1, Actor 2, Actor 3...], etc.
Can anyone give me a hint?
DictVectorizer is the preferred way of handling categorical data that is not already encoded as a Numpy array. For each sample, you can build a bunch of dicts that looks like
[{'Tom Hanks': True, 'Halle Berry': True},
{'Tom Hanks': True, 'Kevin Bacon': True}]
etc. The keys must be strings; the values may be either strings (which are expanded using a one-of-k coding), booleans or numbers. DictVectorizer then transforms these dicts to a matrix that can be fed to a learning algorithm. The matrix will have one column per actor (or other movie feature) in the entire input set. Features not occurring in a dict/sample have an implicit value of zero.
Related
I am trying to create a machine learning model to predict the position of each team, but I am having trouble organizing the data in a way the model can train off of it.
I want the pandas dataframe to look something like this
Where each tournament has team members constantly shifting teams.
And based on the inputted teammates, the model makes a prediction on the team's position. Anyone have any suggestions on how I can make a pandas dataframe like this that a model can use as trainnig data? I'm completely stumped. Thanks in advance!
Coming on to the question as to how to create this sheet, you can easily get the data and store in the format you described above. The trick is in how to use it as training data to your model. We need to convert it in numerical form to be able to be used as training data to any model. As we know that the max team size is 3 in most cases, we can divide the three names in three columns (keep the column blank, if there are less than 3 members in the team). Now we can either use Label encoding or One-hot encoding to convert the names to numbers. You should create a combined list of all three columns to fit a LabelEncoder and then use transform function individually on each column (since the names might be shared in these 3 columns). On label encoding, we can easily use tree based models. One-hot encoding might lead to curse of dimensionality as there will be many names, so I would prefer not to use it for an initial simple model.
NOTE:
If someone else it's wondering about this topic, I understand you're getting deeper in the Data Analysis world, so I did this question before to learn that:
You encode categorical values as INTEGERES only if you're dealing with Ordinal Classes, i.e. College degree, Customer Satisfaction Surveys as an example.
Otherwise if you're dealing with Nominal Classes like, gender, colors or names, you MUST convert them with other methods since they do not specific any numerical order, most known are One-hot Encoding or Dummy variables.
I encorage you to read more about them and hope this has been useful.
Check the link below to see a nice explanation:
https://www.youtube.com/watch?v=9yl6-HEY7_s
This may be a simple question but I think it can be useful for beginners.
I need to run a prediction model on a test dataset, so to convert the categorical variables into categorical codes that can be handled by the random forests model I use these lines with all of them:
Train:
data_['Col1_CAT'] = data_['Col1'].astype('category')
data_['Col1_CAT'] = data_['Col1_CAT'].cat.codes
So, before running the model I have to apply the same procedure to both, the Train and Test data.
And since both datasets have the same categorical variables/columns, I think it will be useful to apply the same categorical codes to each column respectively.
However, although I'm handling the same variables on each dataset I get different codes everytime I use these two lines.
So, my question is, how can I do to get the same codes everytime I convert the same categoricals on each dataset?
Thanks for your insights and feedback.
Usually, how I do this is to do the categorical conversions before the train test split so that I get a neat transformed dataset. Once I do that, I do the train-test split and train the model and test it on the test set.
So I have already performed a multiple linear regression in Python using LinearRegression from sklearn.
My independant variables were all numerical (and so was my dependant one)
But now I'd like to perform a multiple linear regression combining numerical and non numerical independant variables.
Therefore I have several questions:
If I use dummy variables or One-Hot for the non-numerical ones, will I then be able to perform the LinearRegression from sklearn?
If yes, do I have to change some parameters?
If not, how should I perform the Linear Regression?
One thing that bother me is that dummy/one-hot methods don't deal with ordinal variables, right? (Because it shouldn't be encoded the same way in my opinion)
Problem is: Even if I want to encode diffently nominal and ordinal variables,
it seems impossible for Python to tell the difference between both of them?
This stuff might be easy for you but right now as you could tell I'm a little confused so I could really use your help !
Thanks in advance,
Alex
If I use dummy variables or One-Hot for the non-numerical ones, will I then be able to perform the LinearRegression from sklearn?
In fact the model has to be fed exclusively with numerical data, thus you must use OneHot vectors for the categorical data in your input features. For that you can take a look at Scikit-Learn's LabelEncoder and OneHotEncoder.
One thing that bother me is that dummy/one-hot methods don't deal with ordinal variables, right? (Because it shouldn't be encoded the same way in my opinion)
Yes. As you mention one-hot methods don't deal with ordinal variables. One way to work with ordinal features is to create a scale map, and map those features to that scale. Ordinal is a very useful tool for these cases. You can feed it a mapping dictionary according to a predifined scale mapping as mentioned. Otherwise, obviously it randomly assigns integers to the different categories as it has no knowledge to infer any order. From the documentation:
Ordinal encoding uses a single column of integers to represent the classes. An optional mapping dict can be passed in, in this case we use the knowledge that there is some true order to the classes themselves. Otherwise, the classes are assumed to have no true order and integers are selected at random.
Hope this helps.
I have user-item-rating tuples. The users and items are currently both strings (either hash codes or plain text strings, think book or movie titles). The ratings are integers. I'm trying to figure out the data transformation needed to get these ratings into TF to build an autoencoder.
Let's say I have 100K possible items. My thought is that I should feed the model sparse tensors, where each mini-batch would be a set of user-item ratings. Do I need to transform the item strings into integer id's in order to do this? Other than that are there any other details I should know?
For encoding categorical data like sex we normally use LabelEncorder() in scikit learn. But If I'm going to use Tensorflow instead of Scikit Learn, what is the equivalent function or methodology for doing such task? I know that we can do one hot encoding easily with tensorflow, but then it will create labels as 10 , 01 instead of 1 , 0.
There is a package in TensorFlow called tf.feature_columns, that contain 4 methods to create categorical columns from your input data:
categorical_column_with_hash_bucket(...): Hash the input value to a fixed number of categories
categorical_column_with_identity(...): If you have numeric input and you want the value itself to be treated as a categorical column
categorical_column_with_vocabulary_list(...): Outputs a category based on a fixed (memory) list of words
categorical_column_with_vocabulary_file(...): Same as _list but reads the vocabulary from file
The package also provides lots more way of getting your input data to the model. For an overview, see this blogpost written by the developers of the package.