- Pandas DataFrame supports more dtypes than 'int64', 'float64' and 'bool', therefor added a bunch of extra dtypes for the data variable.
- From now on the label variable can be a Pandas DataFrame with the same dtypes as the data variable.
- If label is a Pandas DataFrame will be converted to float.
- If no feature_types is set, the data dtypes will be converted to 'int' or 'float'.
- The feature_names may contain every character except [, ] or <
Changed the name of eval_results to evals_result, so that the naming is the same in training.py and sklearn.py
Made the structure of evals_result the same as in training.py, the names of the keys are different:
In sklearn.py you cannot name your evals_result, but they are automatically called 'validation_0', 'validation_1' etc.
The dict evals_result will output something like: {'validation_0': {'logloss': ['0.674800', '0.657121']}, 'validation_1': {'logloss': ['0.63776', '0.58372']}}
In training.py you can name your multiple evals_result with a watchlist like: watchlist = [(dtest,'eval'), (dtrain,'train')]
The dict evals_result will output something like: {'train': {'logloss': ['0.68495', '0.67691']}, 'eval': {'logloss': ['0.684877', '0.676767']}}
You can access the evals_result using the evals_result() function.
Johan Manders