def data_prep(size=5000, train_file_path='data/train.csv', split=True, remove=None):
"""
Data preprocessing helper function for local running of the ensemble.
INPUTS:
size (int) - number of rows of the train data to use
train_file_path (string) - filepath to location of train data (as csv)
split (bool) - whether to split the data into train and test components or
leave as one unit.
"""
# prepare data for modeling
print("Loading data...")
train = pd.read_csv(train_file_path)
if size > len(train):
df = train
df= train[:size]
print("Preprocessing...")
P = PreProcessor()
if remove:
remove_most = remove
else:
remove_most = ['Unnamed: 0', 'annotations', 'archived', 'author', 'date', \
'distinguished', 'edited','gilded', 'in_reply_to',
'is_first_post', 'link_id', 'link_id_ann', 'majority_link', \
'name', 'parent_id', 'replies', 'retrieved_on', 'saved', \
'score_hidden', 'subreddit', 'title', 'user_reports', \
'ann_1', 'ann_2', 'ann_3']
if split:
# make splits
print("Splitting...")
df_train, df_test = train_test_split(df, test_size=0.25)
df_train = P.run(df_train, 'body', cols_to_drop=remove_most, direct_to_model=False)
df_test = P.run(df_test, 'body', cols_to_drop=remove_most, direct_to_model=False)
return df_train, df_test
else:
df_train = P.run(df, 'body', cols_to_drop=remove_most, direct_to_model=False)
return df_train
def main(size=5000, grid=False):
"""
Composite function designed for running tests.
INPUTS:
size (int) - number of rows of the data set to use
grid (bool) - whether or not to grid search
OUTPUTS:
None
"""
# prepare data for modeling
print("Loading data...")
train = pd.read_csv('data/train.csv')
if size > len(train):
df = train
df = train[:size]
#make splits
print("Splitting...")
df_train, df_test = train_test_split(df, test_size=0.20)
print("Preprocessing...")
P = PreProcessor()
remove_all_but_text = None
remove_most = ['Unnamed: 0', 'annotations', 'archived', 'author', 'date', \
'distinguished', 'edited','gilded', 'in_reply_to',
'is_first_post', 'link_id', 'link_id_ann', 'majority_link', \
'name', 'parent_id', 'replies', 'retrieved_on', 'saved', \
'score_hidden', 'subreddit', 'title', 'user_reports', \
'ann_1', 'ann_2', 'ann_3']
X_train, y_train = P.run(df_train,
'body',
cols_to_drop=remove_most,
direct_to_model=True)
X_test, y_test = P.run(df_test,
'body',
cols_to_drop=remove_most,
direct_to_model=True)
# establish baseline models
baseline_scores = run_baseline_modeling(X_train, y_train, X_test, y_test)
# look at basic NB model results (reduced to NB)
nb_models, NB_base_scores = run_basic_nb_models(X_train, y_train, X_test,
y_test)
if grid:
#run grid search
run_alt_model_tests(X_train, y_train, X_test, y_test)
else:
# look at basic model scores
alt_models, alt_scores = run_alt_models(X_train, y_train, X_test,
y_test)
print("\n\nBaseline Scores: ")
for n, s in zip(['Weighted Guess', 'Guess Most Frequent'],
baseline_scores):
print("{}: {}".format(n, s))
print("Naive Bayes Scores")
for n, s in zip(['Naive Bayes', 'Multinomial Bayes'], NB_base_scores):
print("{}: {}".format(n, s))
print("Other model Scores: ")
for n, s in zip([
'Logistic Regression', 'Random Forest', 'Gradient Boosting',
'Adaboost'
], alt_scores):
print("{}: {}".format(n, s))
