Optunaを使ってみる練習として,Doc2Vecを用いてテキスト分類をするやつをサクっと書きました。出力はモデルのaccuracy,F1と,`pyplot`でのConfusion Matrixを出力します。

出力はモデルのaccuracy,F1と,pyplotでのConfusion Matrixを出力します。


| DOCUMENT_FILE_NAME(id) | LABEL(labels) | |:----------------------:|:-------------:| | foo.txt | bar | | bar.txt | foo |

使い方は,% python -hで。


Source (github)

# Author: Atsuya Kobayashi @atsuya_kobayashi # 2019/02/15 17:20 """Support Vector Document Classifier with doc2vec & Optuna - .csv label file must be in the form of following style |DOCUMENT_FILE_NAME(id)|LABEL(labels)| |----------------------|-------------| | foo.txt | bar | | bar.txt | foo | """ import argparse import itertools import optuna import numpy as np import pandas as pd import matplotlib.pyplot as plt from tqdm import tqdm from sklearn.model_selection import train_test_split, cross_val_score from sklearn.svm import SVC from gensim.models import Doc2Vec from sklearn.metrics import confusion_matrix, accuracy_score, f1_score # parameters PATH_TO_CSVFILE = "" TEXTFILE_TARGET_DIR = "/" PATH_TO_PRETRAINED_DOC2VEC_MODEL = "" N_OPTIMIZE_TRIAL = 20 USE_MORPH_TOKENIZER = False def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print("Normalized confusion matrix") else: print('Confusion matrix, without normalization') print(cm) plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=45) plt.yticks(tick_marks, classes) fmt = '.2f' if normalize else 'd' thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.ylabel('True label') plt.xlabel('Predicted label') plt.tight_layout() return # for Optuna def obj(trial): # C svc_c = trial.suggest_loguniform('C', 1e0, 1e2) # kernel kernel = trial.suggest_categorical('kernel', ['linear', 'poly', 'rbf']) # SVC clf = SVC(C=svc_c, kernel=kernel), y_train) y_pred = clf.predict(X_test) # 3-fold cross validation score = cross_val_score(clf, X_train, y_train, n_jobs=-1, cv=3) accuracy = score.mean() return 1.0 - accuracy if __name__ == "__main__": parser = argparse.ArgumentParser( description='Train a Support Vector Sentence Classifier') parser.add_argument('csv', help='PATH TO CSVFILE') parser.add_argument('dir', help='TEXTFILE TARGET DIRECTORY') parser.add_argument('model', help='PATH TO PRETRAINED DOC2VEC MODEL FILE') parser.add_argument("-N", "--n_trial", dest='n', default=20, type=int, help='N OF OPTIMIZE TRIALS (Default is 20times)') parser.add_argument("-M", "--mecab", dest='mecab', action='store_true', help='USE MECAB Owakati TAGGER') args = parser.parse_args() PATH_TO_CSVFILE = args.csv TEXTFILE_TARGET_DIR = args.dir PATH_TO_PRETRAINED_DOC2VEC_MODEL = args.model N_OPTIMIZE_TRIAL = args.n USE_MORPH_TOKENIZER = args.mecab m = MeCab.Tagger("-Owakati") df = pd.read_csv(PATH_TO_CSVFILE) documents = [] for fname in tqdm(, desc="Reading Files"): with open(TEXTFILE_TARGET_DIR + fname) as f: if USE_MORPH_TOKENIZER: doc = m.parse( else: doc = documents.append(doc) model = Doc2Vec.load(PATH_TO_PRETRAINED_DOC2VEC_MODEL) document_vectors = [model.infer_vector(s) for s in tqdm(documents)] X_train, X_test, y_train, y_test = train_test_split(document_vectors, df.labels, test_size=0.5, random_state=42) study = optuna.create_study() study.optimize(obj, n_trials=N_OPTIMIZE_TRIAL) # fits a model with best params clf = SVC(C=study.best_params["C"], kernel=study.best_params["kernel"]), y_train) y_pred = clf.predict(X_test) # Compute confusion matrix cnf_matrix = confusion_matrix(y_test, y_pred) np.set_printoptions(precision=2) # Plot non-normalized confusion matrix plt.figure() plot_confusion_matrix(cnf_matrix, classes=data.categories, title='Confusion matrix, without normalization') # print result print(f"Acc = {accuracy_score(y_test, y_pred)}") print(f"F1 = {f1_score(y_test, y_pred, average='weighted')}")