linear regression

predict.py

@@ -1,29 +1,35 @@
-import sys
 import pickle
+import sys
 import math
-from normalize import normalize
+import fileinput
 
 model = pickle.load(open("model.pkl", "rb"))
+word_index, vocabulary, weights, words_count = model
 
-pskeptic, vocabulary_size,skeptick_words_total, paranormal_words_total, skeptic_count,paranormal_count = model
+def predict():
+    output = []
+    for line in fileinput.input(openhook=fileinput.hook_encoded("utf-8")):
+        line = line.rstrip()
+        fields = line.split('\t')
+        label = fields[0].strip()
+        document = fields[0]
+        terms = document.split(' ')
+        for term in terms:
+            if term in words_count:
+                words_count[term] += 1
+            else:
+                words_count[term] = 1
+        expected = weights[0]
+        for t in terms:
+            if t in vocabulary:
+                expected +=(words_count[t]/len(words_count)*(weights[word_index[t]]))
+        if expected > 0.9:
+            output.append(1)
+        else:
+            output.append(0)
 
-for line in sys.stdin:
-    document = line.rstrip()
-    terms = normalize(document)
+    with open("out.tsv", "w") as out:
+        for val in output:
+            out.write(str(val)+"\n")
 
-    log_prob_skeptic = math.log(pskeptic)
-    log_prob_paranormal = math.log(1-pskeptic)
-
-    for term in terms:
-        if term not in skeptic_count:
-            skeptic_count[term] = 0
-        if term not in paranormal_count:
-            paranormal_count[term] = 0
-
-        log_prob_skeptic += math.log((skeptic_count[term]+1)/(skeptick_words_total + vocabulary_size))
-        log_prob_paranormal += math.log((paranormal_count[term]+1)/(paranormal_words_total + vocabulary_size))
-
-    if log_prob_skeptic > log_prob_paranormal:
-        print("S")
-    else:
-        print("P")
+predict()

prgram.py

@@ -0,0 +1,97 @@
+#!/usr/bin/env python3
+import pickle
+import fileinput
+import random
+import math
+import random
+import re
+from _collections import defaultdict
+
+def define_vocabulary(file_to_learn_new_words):
+     word_counts = {'count': defaultdict(int)}
+     with open(file_to_learn_new_words, encoding='utf-8') as in_file:
+         for line in in_file:
+             text, timestamp = line.rstrip('\n').split('\t')
+             tokens = text.lower().split(' ')
+             for token in tokens:
+                 word_counts['count'][token] += 1
+     in_file.close()
+     return word_counts
+
+def tokenize_list(string_input):
+    words=[]
+    string=string_input.replace('\\n',' ')
+    text=re.sub(r'\w+:\/{2}[\d\w-]+(\.[\d\w-]+)*(?:(?:\/[^\s/]*))*', '', string)
+    string=''
+    for word in text:
+        string+=word
+    words=re.split(';+|,+|\*+|\n+| +|\_+|\%+|\t+|\[+|\]+|\.+|\(+|\)+|\++|\\+|\/+|[0-9]+|\#+|\'+|\"+|\-+|\=+|\&+|\:+|\?+|\!+|\^+|\·+',string)
+    regex=re.compile(r'http|^[a-zA-Z]$|org')
+    filtered_values=[
+        word
+        for word in words if not regex.match(word)
+    ]
+    filtered_values[:] = (
+        value.lower()
+        for value in filtered_values if len(value)!=0
+    )
+    return filtered_values
+
+def train(vocabulary,input_train,expected_train):
+    learning_rate=0.001
+    learning_precision=0.00001
+    words_vocabulary={}
+    with open(input_train,encoding='utf-8') as input_file, open(expected_train,encoding='utf-8') as expected_file:
+        for line, exp in zip(input_file,expected_file):
+            words_vocabulary[line]=int(exp)
+    weights={}
+    weight={}
+    delta=1
+    iteration=0
+    loss_sum=0.0
+    error=10.0
+    max_iteration=len(vocabulary)
+    for i in vocabulary['count'].keys():
+        weights[i]=random.uniform(-0.01,0.01)
+    while delta>learning_precision and iteration<max_iteration:
+        d,y = random.choice(list(words_vocabulary.items()))
+        y_hat=0
+        tokens=tokenize_list(d)
+        for token in tokens:
+            if token in vocabulary['count'].keys():
+                y_hat += weights[token] * tokens.count(token)
+        delta=(y_hat-y) * learning_rate
+        for word in tokens:
+            if word in words_vocabulary:
+                weights[word] -= (tokens.count(word)) * delta
+        loss = (y_hat - y)**2.0
+        loss_sum += loss
+        if iteration%1000 == 0:
+            if (error>(loss_sum/1000)):
+                weight=weights
+                error=loss_sum/1000
+            loss_sum=0.0
+        iteration += 1
+    input_file.close()
+    expected_file.close()
+    return weight, vocabulary
+
+def prediction(input,output,weights,vocabulary):
+    with open(input,encoding='utf-8') as input_file, open(output,'w+',encoding='utf-8') as output:
+        for line in input_file:
+            y_hat=0
+            tokens=tokenize_list(line)
+            for token in tokens:
+                if token in vocabulary['count'].keys():
+                    y_hat += weights[token] * (token.count(token))
+            if y_hat>0.0:
+                output.write('1\n')
+            else:
+                output.write('0\n')
+    output.close()
+    input_file.close()
+
+vocabulary=define_vocabulary('train/in.tsv');
+weights, words = train(vocabulary,'train/in.tsv','train/expected.tsv')
+prediction('dev-0/in.tsv','dev-0/out.tsv',weights,words)
+prediction('test-A/in.tsv','test-A/out.tsv',weights,words)