84 lines
1.8 KiB
Python
84 lines
1.8 KiB
Python
#generated from jupyter
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# %%
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import lzma
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import sys
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from io import StringIO
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from sklearn.feature_extraction.text import TfidfVectorizer
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import pandas as pd
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import numpy
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pathX = "./train/in.tsv.xz"
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# pathX = "./train/in.tsv"
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pathY = "./train/expected.tsv"
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nrows = 10000
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# %%
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# data = lzma.open(pathX, mode='rt', encoding='utf-8').read()
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# stringIO = StringIO(data)
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# df = pd.read_csv(stringIO, sep="\t", header=None)
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df = pd.read_csv(pathX, sep='\t', nrows=nrows, header=None)
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df = df.drop(df.columns[1], axis=1)
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topics = pd.read_csv(pathY, sep='\t', nrows=nrows, header=None)
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# %%
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print(len(df.index))
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print(len(topics.index))
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# %%
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df.sample()
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# %%
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vectorizer = TfidfVectorizer(lowercase=True, stop_words=['english'])
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X = vectorizer.fit_transform(df.to_numpy().ravel())
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vectorizer.get_feature_names_out()
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# %%
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# vectorizer.transform("Ala ma kotka".lower().split())
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# %%
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df = df.reset_index()
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# %%
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tfidfVector = vectorizer.transform(df[0])
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# %%
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from sklearn.model_selection import train_test_split
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from sklearn.naive_bayes import GaussianNB
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gnb = GaussianNB()
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gnb.fit(tfidfVector.todense(), topics)
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# %%
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testXPath = "./dev-0/in.tsv.xz"
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testYPath = "./dev-0/expected.tsv"
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testX = pd.read_csv(testXPath, sep='\t', nrows=nrows, header=None)
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testY = pd.read_csv(testYPath, sep='\t', nrows=nrows, header=None)
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testXtfidfVector = vectorizer.transform(testX[0])
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# %%
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testXPath = "./test-A/in.tsv.xz"
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testYPath = "./test-A/expected.tsv"
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testX = pd.read_csv(testXPath, sep='\t', nrows=nrows, header=None)
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# testY = pd.read_csv(testYPath, sep='\t', nrows=nrows, header=None)
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testXtfidfVector = vectorizer.transform(testX[0])
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# %%
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pred = gnb.predict(testXtfidfVector.todense())
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print(pred)
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import csv
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with open(testYPath, 'w', newline='') as f_output:
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tsv_output = csv.writer(f_output, delimiter='\n')
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tsv_output.writerow(pred)
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