def get_estimates():
estimate_file = data_file('Estimates','estimates.csv')
if os.path.exists(estimate_file):
v = pandas.read_table(estimate_file,sep=',')
return zip(v.submission, v.estimate)
else:
return []
def _rate_comments(cls, comments):
if not isinstance(comments, list):
comments = [comments]
# import pdb; pdb.set_trace()
# predictions = cls.clf.predict(pd.Series(comments, name="Comment"))
stuff = pd.read_table(data_file('Inputs', "final.csv"), sep=',')
predictions = cls.clf.predict(stuff.Comment.append(
pd.Series(comments)))
return predictions[
-len(comments):] # Hack to get around scale_predictions()
def predict(folds, arguments):
"""
Train on training file, predict on test file.
"""
logging.info("Starting predictions")
print(arguments)
clf = make_pipeline(arguments)
# work out how long to train for final step.
clf.steps[-1][-1].max_iter,estimated_score = choose_n_iterations(folds)
clf.steps[-1][-1].reset_args()
clf.fit(train.Comment, train.Insult) # train the classifier
ypred = clf.predict(test_examples.Comment) # use the trained classifier to classify comments
submission = pandas.DataFrame(dict(Insult=ypred, Comment=test_examples.Comment, Date=test_examples.Date), columns=('Insult', 'Date', 'Comment'))
if arguments.predictions == None:
estimates = get_estimates()
for x in itertools.count(1):
filename = data_file("Submissions", "submission%d.csv" % x)
if os.path.exists(filename):
next
else:
submission.to_csv(filename,index=False)
estimates.append((filename,estimated_score))
save_estimates(estimates)
logging.info('Saved %s' % filename)
break
else:
submission.to_csv(arguments.predictions, index=False)
logging.info('Saved %s' % arguments.predictions)
try:
save_model(clf) # Save the classifier
except:
return(clf)
return(clf)
logging.info("Finished predictions")
sys.path
import pandas as pd
import numpy as np
from insults.util import data_file
import matplotlib.pyplot as plt
from sklearn import linear_model, metrics
df = pd.read_table(data_file('Inputs','train.csv'),sep=',')
df.columns
# ### Feature Engineering
# This is going to be a really basic model to just have as a baseline to compare against more sophisticated models that SHOULD perform much better.
#
# This model will proceed from basic hypothesis:
#
# H: Comments which contains a relatively high number of curse words along with use of the word "you"/"u" are more likely to be insulting.
def load_insults_data(train, test):
df1 = pd.read_table(data_file('Inputs','train.csv'), sep=',')
df2 = pd.read_table(data_file('Inputs','test_with_solutions.csv'), sep=',')
df = pd.concat([df1,df2])
return df
from keras.layers import LSTM, Lambda
from keras.layers import TimeDistributed, Bidirectional
import numpy as np
import keras.callbacks
import sys
import os
from insults.util import data_file
from insults.nn_model.util import setup_logging, LossHistory, binarize, binarize_outshape
from insults.nn_model.plumbing import load_data, load_insults_data, build_examples_with_their_targets
from insults.nn_model.plumbing import sentence_count_per_doc, charset, chars_to_indices_vec
from insults.nn_model.plumbing import shuffle_dataset, dataset_split, strip_quotes
logger = setup_logging(__name__)
INSULTS_TRAIN_DATA_FILE = data_file('Inputs','train.csv')
INSULTS_TEST_DATA_FILE = data_file('Inputs','test_with_solutions.csv')
THIS_FILE = os.path.basename(sys.argv[0]).split('.')[0]
total = len(sys.argv)
cmdargs = str(sys.argv)
logger.info("Script name: %s" % str(sys.argv[0]))
insults_data = load_insults_data(INSULTS_TRAIN_DATA_FILE, INSULTS_TEST_DATA_FILE)
comments, targets = build_examples_with_their_targets(insults_data.Comment, insults_data.Insult)
comments = strip_quotes(comments)
num_sent = sentence_count_per_doc(comments)
chars = charset(comments)
def make_full_training():
df1 = pandas.read_table(data_file('Inputs','train.csv'),sep=',')
df2 = pandas.read_table(data_file('Inputs','test_with_solutions.csv'),sep=',')
df = pandas.concat([df1,df2])
df.to_csv(data_file('Inputs','fulltrain.csv'),index=False)
def save_estimates(se):
estimate_file = data_file('Estimates', 'estimates.csv')
submissions,estimates = zip(*se)
pandas.DataFrame(dict(submission=submissions, estimate=estimates)).to_csv(estimate_file, index=False)
def test_data_file():
path = util.data_file("test_category")
path_two = util.data_file("test_category", name="test_name")
assert path == 'insults/Data/test_category'
assert path_two == 'insults/Data/test_category/test_name'
def test_data_directory():
path = util.data_file("test_category")
assert path == 'insults/Data/test_category'
