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Linear regression higher F1.0 lower accuracy

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This commit is contained in:
Th3NiKo 2020-04-06 14:01:32 +02:00
parent d7040c9bc6
commit db398db388
4 changed files with 4461 additions and 4456 deletions
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4414
dev-0/out.tsv
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13
predict.py
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@ -8,6 +8,8 @@ from tokenizer import tokenize
#Load model #Load model
model = pickle.load(open("model.pkl","rb")) model = pickle.load(open("model.pkl","rb"))
weights, word_to_index_mapping, word_count = model weights, word_to_index_mapping, word_count = model
sum = 0
counter = 0
for line in sys.stdin: for line in sys.stdin:
document = line.rstrip() document = line.rstrip()
@ -17,9 +19,12 @@ for line in sys.stdin:
y_predicted = weights[0] y_predicted = weights[0]
for word in terms: for word in terms:
y_predicted += weights[word_to_index_mapping.get(word,0)] * (word_count.get(word,0) / len(word_count)) y_predicted += weights[word_to_index_mapping.get(word,0)] * log(word_count.get(word,0) / len(word_count) + 1)
sum += y_predicted
if y_predicted <= 0.5: counter += 1
if y_predicted <= 0:
print(0) print(0)
else: else:
print(1) print(1)
#print(sum / counter)

4484
test-A/out.tsv
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6
train.py
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@ -76,7 +76,7 @@ def train():
#With get u can avoid missing words and replace them with value u want #With get u can avoid missing words and replace them with value u want
#Weights replace value doesnt matter if word is missing cause word_count will give 0 #Weights replace value doesnt matter if word is missing cause word_count will give 0
for word in actual_x: for word in actual_x:
y_predicted += weights[word_to_index_mapping.get(word,0)] * (word_count.get(word,0) / len(word_count)) y_predicted += weights[word_to_index_mapping.get(word,0)] * log(word_count.get(word,0) / len(word_count) + 1)
#Cost count. Check how good was our prediction #Cost count. Check how good was our prediction
Loss = (y_predicted - actual_y) ** 2.0 Loss = (y_predicted - actual_y) ** 2.0
@ -94,9 +94,9 @@ def train():
weights[0] = weights[0] - delta weights[0] = weights[0] - delta
for word in actual_x: for word in actual_x:
if word in word_to_index_mapping: if word in word_to_index_mapping:
weights[word_to_index_mapping[word]] -= ((word_count[word] / len(word_count)) * delta) weights[word_to_index_mapping[word]] -= (log(word_count[word] / len(word_count) + 1) * delta)
if Loss_sum_counter > 10000000: if Loss_sum_counter > 7000000:
break break