change script for fine-tuning alpha

run.py

@@ -1,5 +1,6 @@
 import pandas as pd
 import csv
+import sys
 import regex as re
 from collections import Counter, defaultdict
 from nltk import trigrams, word_tokenize
@@ -17,7 +18,7 @@ class Model():
         self.vocab = set()
 
     def train(self, data):
-        for _, row in data.iterrows():
+        for index, row in data.iterrows():
             text = clean_text(str(row['text']))
             words = word_tokenize(text)
             for w1, w2, w3 in trigrams(words, pad_right=True, pad_left=True):
@@ -26,6 +27,9 @@ class Model():
                     self.vocab.add(w2)
                     self.vocab.add(w3)
                     self.probs[(w1, w3)][w2] += 1
+            # limit number of data rows used for training
+            if index > 10000:
+                break
 
         for w1_w3 in self.probs:
             total_count = float(sum(self.probs[w1_w3].values()))
@@ -47,14 +51,18 @@ class Model():
 
         remaining_prob = 1 - total_prob
             
-        if remaining_prob == 0:
-            remaining_prob = 0.01
-            
         str_prediction += f':{remaining_prob}'
         
         return str_prediction
 
 
+# check arguments
+if len(sys.argv) != 2:
+    print('Wrong number of arguments. Expected 1 - alpha smoothing parameter.')
+    quit()
+else:
+    alpha = sys.argv[1]
+
 # load training data
 train_data = pd.read_csv('train/in.tsv.xz', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)
 train_labels = pd.read_csv('train/expected.tsv', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)
@@ -66,7 +74,7 @@ train_data['text'] = train_data[6] + train_data[0] + train_data[7]
 train_data = train_data[['text']]
 
 # init model with given aplha
-model = Model(0.01)
+model = Model(alpha)
 
 # train model probs
 model.train(train_data)