* Add format to the params accepted by DumpModel
Currently, only the test format is supported when trying to dump
a model. The plan is to add more such formats like JSON which are
easy to read and/or parse by machines. And to make the interface
for this even more generic to allow other formats to be added.
Hence, we make some modifications to make these function generic
and accept a new parameter "format" which signifies the format of
the dump to be created.
* Fix typos and errors in docs
* plugin: Mention all the register macros available
Document the register macros currently available to the plugin
writers so they know what exactly can be extended using hooks.
* sparce_page_source: Use same arg name in .h and .cc
* gbm: Add JSON dump
The dump_format argument can be used to specify what type
of dump file should be created. Add functionality to dump
gblinear and gbtree into a JSON file.
The JSON file has an array, each item is a JSON object for the tree.
For gblinear:
- The item is the bias and weights vectors
For gbtree:
- The item is the root node. The root node has a attribute "children"
which holds the children nodes. This happens recursively.
* core.py: Add arg dump_format for get_dump()
* make DMatrix._init_from_npy2d only copy data when necessary
When creating DMatrix from a 2d ndarray, it can unnecessarily copy the input data. This can be problematic when the data is already very large--running out of memory. The copy is temporary (going out of scope at the end of this function) but it still adds to peak memory usage.
``numpy.array`` copies its input no matter what by default. By adding ``copy=False``, it will only do so when necessary. Since XGDMatrixCreateFromMat is readonly on the input buffer, this copy is not needed.
Also added comments explaining when a copy can happen (if data ordering/layout is wrong or if type is not 32-bit float).
* remove whitespace
* added new function to calculate other feature importances
* added capability to plot other feature importance measures
* changed plotting default to fscore
* added info on importance_type to boilerplate comment
* updated text of error statement
* added self module name to fix call
* added unit test for feature importances
* style fixes
This error message can be hard to understand when there are several fields, as shown in the example below. This improves the error message, letting the user know which fields were unexpected or missing.
import xgboost as xgb
import pandas as pd
train = pd.DataFrame({'a':[1], 'b':[2], 'c':[3], 'd':[4], 'f':[2], 'g':2, 'etc etc etc':[11]})
dtrain = xgb.DMatrix(train.drop('d', axis=1), train.d)
test = pd.DataFrame({'a':[1], 'b':[2], 'c':[1], 'd':[4], 'e':[2], 'f':[2], 'g':2, 'etc etc etc':[11]})
dtest = xgb.DMatrix(test)
modl = xgb.train({}, dtrain)
modl.predict(dtest)
# ValueError: feature_names mismatch: [u'a', u'b', u'c', u'etc etc etc', u'f', u'g'] [u'a', u'b', u'c', u'd', u'e', u'etc etc etc', u'f', u'g']
- allows feval to return a list of tuples (name, error/score value)
- changed behavior for multiple eval_metrics in conjunction with
early_stopping: Instead of raising an error, the last passed evel_metric
(or last entry in return value of feval) is used for early stopping
- allows list of eval_metrics in dict-typed params
- unittest for new features / behavior
documentation updated
- example for assigning a list to 'eval_metric'
- note about early stopping on last passed eval metric
- info msg for used eval metric added
- 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 <
Currently `pip install xgboost` will raise traceback like this
```
Traceback (most recent call last):
File "<string>", line 20, in <module>
File "/tmp/pip-build-IAdqYE/xgboost/setup.py", line 20, in <module>
import xgboost
File "./xgboost/__init__.py", line 8, in <module>
from .core import DMatrix, Booster
File "./xgboost/core.py", line 12, in <module>
import numpy as np
ImportError: No module named numpy
```
We should avoid importing numpy in setup.py and let pip install numpy and scipy automatically.
That's what `install_requires` for.
