forked from kubapok/retroc2
51 lines
1.2 KiB
Python
51 lines
1.2 KiB
Python
import lzma
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import math
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.linear_model import LinearRegression
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from sklearn.pipeline import make_pipeline
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from sklearn.metrics import mean_squared_error
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import pandas as pd
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X_train = []
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Y_train = []
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stop = 0
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with lzma.open('train/train.tsv.xz', 'rt', encoding="utf-8") as f:
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data = pd.read_csv(f, sep='\t', names=['Begin', 'End', 'Text'])
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data = data[['Text', 'End']]
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data = data[0:50000]
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X = data['Text']
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y = data['End']
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model = make_pipeline(TfidfVectorizer(), LinearRegression())
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model.fit(X, y)
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def readFile(filename):
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X_dev = []
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with open(filename, 'r', encoding="utf-8") as dev_in:
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for line in dev_in:
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text = line.split("\t")[0].strip()
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X_dev.append(text)
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return X_dev
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def writePred(filename, predictions):
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with open(filename, "w") as out_file:
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for pred in predictions:
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out_file.write(str(pred) + "\n")
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x = readFile('dev-0/in.tsv')
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pred = model.predict(x)
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writePred('dev-0/out.tsv',pred)
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x = readFile('dev-1/in.tsv')
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pred = model.predict(x)
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writePred('dev-1/out.tsv',pred)
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x = readFile('test-A/in.tsv')
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pred = model.predict(x)
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writePred('test-A/out.tsv',pred) |