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Py3 #2

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jrings Dec 16, 2014
14e1640
brackets for print commands
jrings Dec 16, 2014
f767aa0
Missed one print statement
jrings Dec 16, 2014
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4 changes: 2 additions & 2 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@ train = [
]
v = DictVectorizer()
X = v.fit_transform(train)
print X.toarray()
print(X.toarray())
[[ 19. 0. 0. 0. 1. 1. 0. 0. 0.]
[ 33. 0. 0. 1. 0. 0. 1. 0. 0.]
[ 55. 0. 1. 0. 0. 0. 0. 1. 0.]
Expand Down Expand Up @@ -102,7 +102,7 @@ Training RMSE: 0.43527
# Evaluate
preds = fm.predict(X_test)
from sklearn.metrics import mean_squared_error
print "FM RMSE: %.4f" % mean_squared_error(y_test,preds)
print("FM RMSE: %.4f" % mean_squared_error(y_test,preds))
FM RMSE: 0.9253

```
11 changes: 6 additions & 5 deletions pylibfm.py
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@@ -1,7 +1,9 @@
import random

import numpy as np
from sklearn import cross_validation
import random
from pyfm_fast import FM_fast, CSRDataset

from pyfm_fast import CSRDataset, FM_fast

LEARNING_RATE_TYPES = {"optimal": 0, "invscaling": 1, "constant": 2}
TASKS = {"regression": 0, "classification" : 1}
Expand Down Expand Up @@ -172,7 +174,7 @@ def fit(self, X, y):

# use sklearn to create a validation dataset for lambda updates
if self.verbose == True:
print "Creating validation dataset of %.2f of training for adaptive regularization" % self.validation_size
print("Creating validation dataset of %.2f of training for adaptive regularization" % self.validation_size)
X_train, validation, train_labels, validation_labels = cross_validation.train_test_split(
X, y, test_size=self.validation_size)
self.num_attribute = X_train.shape[1]
Expand Down Expand Up @@ -212,7 +214,7 @@ def fit(self, X, y):
# report epoch information
if self.verbose == True:
print("-- Epoch %d" % (epoch + 1))
print "Train RMSE: %.5f" % (self.sumloss / self.count)
print("Train RMSE: %.5f" % (self.sumloss / self.count))

def predict(self, X):
"""Predict using the factorization machine
Expand All @@ -238,4 +240,3 @@ def _make_dataset(X, y_i):
sample_weight = np.ones(X.shape[0], dtype=np.float64, order='C') # ignore sample weight for the moment
dataset = CSRDataset(X.data, X.indptr, X.indices, y_i, sample_weight)
return dataset