from testfm.evaluation.evaluator import Evaluator
from pkg_resources import resource_filename
from testfm.evaluation.parameter_tuning import ParameterTuning
if __name__ == "__main__":
eval = Evaluator(
) # Call this before loading the data to save memory (fork of process takes place)
# Prepare the data
df = pd.read_csv(resource_filename(testfm.__name__,
'data/movielenshead.dat'),
sep="::",
header=None,
names=['user', 'item', 'rating', 'date', 'title'])
print df.head()
training, testing = testfm.split.holdoutByRandom(df, 0.9)
print "Tuning the parameters."
tr, validation = testfm.split.holdoutByRandom(training, 0.7)
pt = ParameterTuning()
pt.set_max_iterations(100)
pt.set_z_value(90)
tf_params = pt.get_best_params(TensorCoFi, tr, validation)
print tf_params
tf = TensorCoFi()
tf.set_params(**tf_params)
tf.fit(training)
print tf.get_name().ljust(50),
print eval.evaluate_model(tf, testing, all_items=training.item.unique())
