Define the list of elements in the KairosDay class

[tomespel]

Following #4 . Here are the elements of the class KairosDay.

• __id (int) corresponds to the target id.
• _asset (int) corresponds to the asset id.
• __date (int) corresponds to the date.
• __volatility corresponds to the list of double with the volatility values.
• __returns corresponds to the list of double with the returns values.
• __isClassified (bool) indicates if the asset has received it local volatility classification.
• __localClassification (string) specifies the local classification hlv, mlv or llv. States None otherwise.
• __classificationDetails (dict) specifies the local classification probabilities in the form of a dictionary {'method': double(probability)}.
• __features (dict) specifies the features list in the form of a dictionary {'featureName': list(featureValuesList)}.

[mehditomas]

Some notes:

• It is clever to split the dataset by date and asset as this will be useful in the next steps; I would also
• For feature engineering and manipulation, it would be more practical to merge volatility, returns, etc, in the form of a list of features instead of considering them separately. I need to think about it, but I'm not yet convinced that we need the values of each of those features in our KairosDay class.
• I think we could go further with your ideas and drop the localClassification element and encode everything within the classificationDetails, especially since I don't think there will be a need to access them in this format and it would simplify the class

[tomespel]

• Ok, we will move forward using this scheme.
• Fine, let me know how it turns out. I definitely agree that it would me more sense to place them among the other features.
• This looks redundant, but it makes the sorting of the days depending on local volatility much easier. I think we should keep it because accessing a dictionary takes more time that accessing a variable. I will put some thoughts in it.

Am 05.03.2018 um 11:26 schrieb Mehdi Tomas notifications@github.com:

Some notes:

It is clever to split the dataset by date and asset as this will be useful in the next steps; I would also For feature engineering and manipulation, it would be more practical to merge volatility, returns, etc, in the form of a list of features instead of considering them separately. I need to think about it, but I'm not yet convinced that we need the values of each of those features in our KairosDay class. I think we could go further with your ideas and drop the localClassification element and encode everything within the classificationDetails, especially since I don't think there will be a need to access them in this format and it would simplify the class — You are receiving this because you were assigned. Reply to this email directly, view it on GitHub, or mute the thread.

[tomespel]

Replying to your second point, I will implement a new structure. All the values will be stored into a single vector (ˋlistˋ format), a Turing-like data ribbon. A dictionary will keep track of the location on the ribbon (the adresses as a pair of ˋintˋ with first value and length). This will increase the output time when extracting to a ML tool, while preserving the ability to keep track of the elements. I’ll try to implement this before tomorrow night, it shouldn’t be long.

Am 05.03.2018 um 11:26 schrieb Mehdi Tomas notifications@github.com:

Some notes:

It is clever to split the dataset by date and asset as this will be useful in the next steps; I would also For feature engineering and manipulation, it would be more practical to merge volatility, returns, etc, in the form of a list of features instead of considering them separately. I need to think about it, but I'm not yet convinced that we need the values of each of those features in our KairosDay class. I think we could go further with your ideas and drop the localClassification element and encode everything within the classificationDetails, especially since I don't think there will be a need to access them in this format and it would simplify the class — You are receiving this because you were assigned. Reply to this email directly, view it on GitHub, or mute the thread.

[tomespel]

Here are the elements of the class KairosDay.

• __id (int) corresponds to the target id.
• __asset (int) corresponds to the asset id.
• __date (int) corresponds to the date.
• __features (list) is the continuous list of all features in the class.
• __featuresIndex (dict) specifies the position of the feature in __features (list) in the form of a dictionary {'featureName': (featureLocation (int), featureLength (int))}.
• __localClassification (string) specifies the local classification hlv, mlv or llv. States None otherwise.
• __classificationDetails (dict) specifies the local classification probabilities in the form of a dictionary {'method': double(probability)}.