reddit p|s s478874

run.py

@@ -0,0 +1,91 @@
+import lzma
+import sys
+from io import StringIO
+from sklearn.feature_extraction.text import TfidfVectorizer
+import pandas as pd
+import csv
+import numpy
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import GaussianNB
+
+
+pathX = "./train/in.tsv.xz"
+pathY = "./train/expected.tsv"
+nrows = 10000
+
+
+def load_data(path):
+    in_df = pd.read_csv(f'{path}/in.tsv.xz', sep='\t', nrows=nrows, header=None)
+    try:
+        exp_df = pd.read_csv(f'{path}/expected.tsv', sep='\t', nrows=nrows, header=None)
+    except:
+        exp_df = None
+    return in_df, exp_df
+
+
+def write_res(data, path):
+    with open(path, 'w') as f:
+        for line in data:
+            f.write(f'{line}\n')
+    print(f"Data written {path}/out.tsv")
+
+
+
+
+def main():
+    in_df, exp_df = load_data('train')
+    in_df = in_df.drop(in_df.columns[1], axis=1)
+    
+    vectorizer = TfidfVectorizer(lowercase=True, stop_words=['english'])
+    X = vectorizer.fit_transform(in_df.to_numpy().ravel())
+    vectorizer.get_feature_names_out()
+
+    # in_df = in_df.reset_index()
+
+    tfidfVector = vectorizer.transform(in_df[0])
+    gnb = GaussianNB()
+    gnb.fit(tfidfVector.todense(), exp_df)
+
+    paths = ['dev-0', 'test-A']
+
+    for path in paths:
+        x = load_data(path)
+        vec = vectorizer.transform(x[0][0])
+    
+        result = gnb.predict(vec.todense())
+        write_res(result, f'{path}/expected.tsv')
+
+
+
+if __name__ == '__main__':
+    main()
+
+
+
+# testXPath = "./dev-0/in.tsv.xz"
+# testYPath = "./dev-0/expected.tsv"
+
+# testX = pd.read_csv(testXPath, sep='\t', nrows=nrows, header=None)
+
+# testY = pd.read_csv(testYPath, sep='\t', nrows=nrows, header=None)
+# testXtfidfVector = vectorizer.transform(testX[0])
+
+
+
+# testXPath = "./test-A/in.tsv.xz"
+# testYPath = "./test-A/expected.tsv"
+
+# testX = pd.read_csv(testXPath, sep='\t', nrows=nrows, header=None)
+
+# # testY = pd.read_csv(testYPath, sep='\t', nrows=nrows, header=None)
+# testXtfidfVector = vectorizer.transform(testX[0])
+
+
+
+# pred = gnb.predict(testXtfidfVector.todense())
+# print(pred)
+
+
+# with open(testYPath, 'w', newline='') as f_output:
+#     tsv_output = csv.writer(f_output, delimiter='\n')
+#     tsv_output.writerow(pred)