# The optimal cross validation values should be found from the csv file
try:
with open(gridsearchcv_file_name, "r") as csvfile:
reader = csv.reader(csvfile)
for record in reader:
if feature_scaling == str(record[0]) and \
feature_selection == (record[1]=="True") and \
clf_id == int(record[2]):
run_grid = json_loads_byteified(record[3])
print "Fetched best GridSearchCV values: ", run_grid
except:
print "Could not read grid filename: ", gridsearchcv_file_name
print "Defaulting to parameter standard settings"
# Check if we do have now parameters for our algorithm and set them
if len(run_grid) > 0:
algorithm.set_params(**run_grid)
# Just use the standard algorithm
wrapped_algorithm = algorithm
elif gridsearchcv == "out":
# The optimal grid search values should be found and written (later)
# to the CSV file
wrapped_algorithm = GridSearchCV(algorithm, search_grid,
scoring='precision')
pipe.extend([('Algorithm',wrapped_algorithm)])
# Put all the pipeline steps into an pipeline object and assign to "clf" for
# further processing and later on, serialization
clf = Pipeline(steps=pipe)
# ------------
