cols = input()
cols_list.append(cols)
data.scale_data(cols_list[:-1])
cols_list = data.get_cols()
y_column_name = input("enter y column name: ")
X_train, X_test, y_train, y_test = data.spilt_data(y_column_name)
model_type = input("Enter R for Regression and C for Classification: ")
if model_type == "C":
print("Your options are: " + str(Classifier_list)) #add mode list
modelname = input("Enter model to be used: ")
classifier = Classification(X_train, X_test, y_train, y_test, modelname)
classifier.predict()
classifier.accuracy()
classifier.save_model()
elif model_type == 'R':
print("Your options are: " + str(Regressor_list)) #add mode list
modelname = input("Enter model to be used, use A for all")
if modelname == "A":
for modelname in Regressor_list:
regressor = Regression(X_train, X_test, y_train, y_test, modelname)
regressor.predict()
regressor.accuracy()
regressor.save_model()
else:
regressor = Regression(X_train, X_test, y_train, y_test, modelname)
regressor.predict()
regressor.accuracy()
def main():
parser = OptionParser()
parser.add_option("--stemmer-language", dest="stemmer_language", help="Language for SnowballStemmer", default="english")
parser.add_option('-i', action="store_true", dest="ignore_stopwords_stemmer", help="Ignore stopwords in stemmer, default false", default=False)
parser.add_option("--stopwords-language", dest="stopwords_language", help="Language for stopwords")
parser.add_option("-k", action="store_true", dest="keep_stopwords", help="Keep stopwords, default remove", default=False)
parser.add_option('--load-classifier', dest="load_classifier_file_path", help="Specify load classifiers file")
parser.add_option('--create-classifier', dest="create_classifier", help="File for training set")
parser.add_option('--row-training-set', dest="row_training_set", help="Number of row for training set", default=1000)
parser.add_option('-r', action="store_true", dest="random_row_training_set", help="Get random row from training set file", default=False)
parser.add_option('--text-field', dest="text_field", help="text field in json file", default="text")
parser.add_option('--word-tokenize-language', dest="word_tokenize_language", help="Word tokenize language", default="english")
parser.add_option('--classification-field', dest="classification_field", help="Classification field in json data", default="category")
parser.add_option('--dump-classifier', dest="dump_classifier", help="Dump classifier file", default=False)
parser.add_option('-a', action="store_true", dest="calculate_accuracy", help="Calculate accuracy", default=False)
parser.add_option('--test-file-path', dest="test_file_path", help="Test file path")
parser.add_option('--row-test-set', dest="row_test_set", help="Number of row for test set", default=500)
parser.add_option('--random-row-test-set', action="store_true", dest="random_row_test_set", help="Get random row from test set file", default=False)
parser.add_option('--test-text-field', dest="test_text_field", help="text field in json test file", default="text")
parser.add_option('--test-classification-field', dest="test_classification_field", help="classificaion field in json test file", default="category")
parser.add_option('--classify', dest="classify_text", help="classify text", default=False)
(options, args) = parser.parse_args(sys.argv)
cl = Classification(
stemmer_language=options.stemmer_language,
stopwords_language=options.stopwords_language,
ignore_stopwords_stemmer=options.ignore_stopwords_stemmer,
)
if options.load_classifier_file_path:
cl.load_classifier(load_classifier_file_path=options.load_classifier_file_path)
elif options.create_classifier:
cl.create_and_train_classifier(
training_file_path=options.create_classifier,
keep_stopwords=options.keep_stopwords,
row_training_set=options.row_training_set,
random_row_training_set=options.random_row_training_set,
text_field=options.text_field,
word_tokenize_language=options.word_tokenize_language,
classification_field=options.classification_field
)
if options.dump_classifier:
cl.dump_classifier(options.dump_classifier)
if options.calculate_accuracy:
cl.accuracy(
test_file_path=options.test_file_path,
keep_stopwords=options.keep_stopwords,
row_test_set=options.row_test_set,
random_row_test_set=options.random_row_test_set,
text_field=options.test_text_field,
word_tokenize_language=options.word_tokenize_language,
classification_field=options.test_classification_field
)
if(options.classify_text):
cl.classify(
text=options.classify_text,
keep_stopwords=options.keep_stopwords,
word_tokenize_language=options.word_tokenize_language
)