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returned support for sparse matrixes (which was depricated) #310

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returned support for sparse matrixes (which was depricated) #310

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33 changes: 26 additions & 7 deletions python-package/xlearn/_sklearn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
import tempfile
import warnings
import numpy as np
import scipy

from .xlearn import create_linear, create_fm, create_ffm
from .data import DMatrix
Expand Down Expand Up @@ -209,6 +210,8 @@ def fit(self, X, y=None, fields=None,
self._XLearnModel = create_ffm()
else:
raise Exception('model_type must be fm, ffm or lr')

temp_train_file = tempfile.NamedTemporaryFile(delete=True)

if y is None:
assert isinstance(X, str), 'X must be a string specifying training file location' \
Expand All @@ -223,7 +226,7 @@ def fit(self, X, y=None, fields=None,
self.fields = fields

# convert data into libsvm/libffm format for training
train_set = DMatrix(X, y, self.fields)
train_set = self._apply_correct_transformation(X, y, temp_train_file.name, fields=self.fields)
self._XLearnModel.setTrain(train_set)

# TODO: find out what task need to set sigmoid
Expand Down Expand Up @@ -252,14 +255,14 @@ def fit(self, X, y=None, fields=None,
else:
if not (isinstance(eval_set, list) and len(eval_set) == 2):
raise Exception('eval_set must be a 2-element list')

temp_val_file = tempfile.NamedTemporaryFile(delete=True)
# extract validation data
X_val, y_val = check_X_y(eval_set[0], eval_set[1],
accept_sparse=['csr'],
y_numeric=True,
multi_output=False)

validate_set = DMatrix(X_val, y_val, self.fields)
validate_set = self._apply_correct_transformation(X_val, y_val, temp_val_file.name, fields=self.fields)
self._XLearnModel.setValidate(validate_set)

# set up files for storing weights
Expand All @@ -270,6 +273,9 @@ def fit(self, X, y=None, fields=None,

# acquire weights
self._parse_weight(self._temp_weight_file.name)

# remove temporary files for training
self._remove_temp_file(temp_train_file)

def predict(self, X):
""" Generate prediction using feature matrix X
Expand All @@ -278,17 +284,21 @@ def predict(self, X):
Feature matrix
:return: prediction
"""


temp_test_file = tempfile.NamedTemporaryFile(delete=True)

if isinstance(X, str):
self._XLearnModel.setTest(X)
else:
X = check_array(X, accept_sparse=['csr'])
test_set = DMatrix(X, None, self.fields)
test_set = self._apply_correct_transformation(X, None, temp_test_file.name, fields = self.fields)
self._XLearnModel.setTest(test_set)

# generate output
pred = self.get_model().predict(self._temp_model_file.name)

# remove temporary test data
self._remove_temp_file(temp_test_file)

return pred

def feature_importance_(self):
Expand Down Expand Up @@ -318,6 +328,15 @@ def _convert_data(self, X, y, filepath, fields=None):
except:
raise Exception('Failed to convert feature matrix X and label y to xlearn data format')

def _apply_correct_transformation(self, X, y, filepath, fields=None):
#check type of object and apply correct transformation
if (scipy.sparse.issparse(X)):
self._convert_data(X, y, filepath, fields = fields)
return filepath
else:
coverted_data = DMatrix(X, y, fields)
return coverted_data

def _parse_weight(self, file_name):
# estimate number of features from txt file
num_lines = sum(1 for line in open(file_name))
Expand Down