Change auto_cast in SuperVectorizer

[LeoGrin]

Uses pandas TextParser (used in read_csv, as per @GaelVaroquaux suggestion in #234) instead of convert_dtypes in the SuperVectorizer _auto_cast method. This has several advantages:

• Type detection works on string columns, e.g ["3", "2", "1"] would get detected as integer. Solves Handle string in auto_cast (SuperVectorizer) #234
• Missing values encoded as strings get treated as missing values, and don't prevent type detection. For instance, [1, 2, "", 4] is detected as integers. (solves Replace strings which indicates missing values by NaNs (for SuperVectorizer) #231). Which strings get detected as Nans is easy to change (see below).
• Datetime columns are converted to datetimes, and the format is inferred by Pandas. Solve the first part of Handling date columns in SuperVectorizer #233.

Some comments:

• TODO: I'd like to enable the user to specify a specific date format if he wants to.
• Strings detected as NaNs are the default STR_NA_VALUES, and I've added [None, " ", "?", "..."]. I think we could find a more principled way to choose which strings to include.
• I haven't properly measured the speed of the conversion.
• To use TextParser, I need to convert the input data into a list of list. I don't think this is a problem, but tell me if you think otherwise.
• All variables which are not numerics or datetimes are converted to object (and thus handled as categorical in the SuperVectorizer).
• Right now, if a datetime column contains mixed timezones, it is encoded as an object.

[LilianBoulard]

Thanks for the PR, it looks nice !

[LilianBoulard]

LGTM !

[LeoGrin]

In trying to take into account @GaelVaroquaux's comment, I've actually realized that instead of using TextParser, it would be simpler to go back to @LilianBoulard's version of autocast, and:

• replace convert_dtypes by pd.to_numeric and pd.to_datetime
• Replace every missing value with np.nan before type conversion

[GaelVaroquaux]

Very interesting!! You know these questions better than us, now, so your input is super useful. Tell us when you want a second review.

[GaelVaroquaux]

Darn, the tests errors are showing us that the strategy here is probably quite sensitive to the version of pandas.

[LilianBoulard]

Looks good !

[GaelVaroquaux]

A few thoughts (I'm too tired to finish tonight).

[GaelVaroquaux]

LGTM. Merging. Thanks a lot!!