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| import sys | |
| import os | |
| import yaml | |
| import pandas as pd | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| import pickle | |
| # read command line params | |
| if len(sys.argv) != 3: | |
| sys.stderr.write('Arguments error. Usage:\n') | |
| sys.stderr.write( | |
| '\tpython featurize.py data-dir-path features-dir-path\n' | |
| ) | |
| sys.exit(1) | |
| data_path = sys.argv[1] | |
| features_path = sys.argv[2] | |
| os.makedirs(features_path, exist_ok=True) | |
| train_input_file = os.path.join(data_path, 'train.csv') | |
| test_input_file = os.path.join(data_path, 'test.csv') | |
| # read the data from file | |
| df_train = pd.read_csv(train_input_file) | |
| df_test = pd.read_csv(test_input_file) | |
| def extract_column(column, df_path): | |
| df = get_df(df_path) | |
| corpus = df[[column]] | |
| return corpus | |
| def get_train_and_test_corpus(df_1, df_2): | |
| corpus_train = df_1["text"] | |
| corpus_test = df_2["text"] | |
| return corpus_train.append(corpus_test) | |
| def append_labels_and_save_pkl(df, tfidf_matrix, filename): | |
| output_file = os.path.join(features_path, filename) | |
| target = df[["target"]] | |
| output = pd.concat([pd.DataFrame(tfidf_matrix.toarray()), target], axis=1) | |
| with open(output_file, 'wb') as f: | |
| pickle.dump(output, f) | |
| vectorizer = TfidfVectorizer() | |
| # we need to fit the vectorizer with both train and test data | |
| corpus = get_train_and_test_corpus(df_train, df_test) | |
| vectorizer.fit(corpus) | |
| # transform the data | |
| train_matrix = vectorizer.transform(df_train["text"]) | |
| test_matrix = vectorizer.transform(df_test["text"]) | |
| # save data to pickle (appending the labels column) | |
| append_labels_and_save_pkl(df_train, train_matrix, 'train.pkl') | |
| append_labels_and_save_pkl(df_test, test_matrix, 'test.pkl') |
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