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trainsvm.py
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trainsvm.py
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from sklearn import svm
import pandas as pd
from sklearn import tree
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import f1_score
from sklearn.preprocessing import MultiLabelBinarizer, LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.multioutput import MultiOutputClassifier
from sklearn.metrics import classification_report, confusion_matrix
import numpy as np
def fit(s):
darklyrics = pd.read_csv('darklyrics-proc-tokens-single.csv',
converters={'tokens': lambda x: x.strip("[]").replace("'", "").split(", ")})
corpus = darklyrics.apply(lambda x: " ".join(x['tokens']), axis=1)
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
X = X.todense()
X = np.array(X)
labels = darklyrics['genre']
print('Fase Split')
X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2, random_state=0)
encoder = LabelEncoder()
y_train = encoder.fit_transform(y_train)
y_test = encoder.transform(y_test)
if s == "tree":
clf = tree.DecisionTreeClassifier()
elif s == "svm":
clf = svm.SVC()
elif s == "lda":
clf = LinearDiscriminantAnalysis()
else:
print("Choose among 'svm', 'tree', 'lda'")
return 0
print('Fitting')
clf.fit(X_train, y_train)
print('Predict')
y_pred = clf.predict(X_test)
print('Result')
score_macro = f1_score(y_test, y_pred, average="macro")
score_micro = f1_score(y_test, y_pred, average="micro")
print("F1_macro:{0}, F1_micro:{1}".format(score_macro, score_micro))
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))
#clf = MultiOutputClassifier(SVC()).fit(X_train, y_train)
if __name__ == '__main__':
fit("tree")
fit("svm")
fit("lda")