69 lines
1.5 KiB
Python
69 lines
1.5 KiB
Python
from model import m
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from preprocessor import fr
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import pandas as pd
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import numpy as np
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import pickle
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import time
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from sys import exit
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from tensorflow.keras.utils import pad_sequences as kps
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from matplotlib import pyplot as plt
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from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score
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from tqdm import tqdm
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tqdm.pandas()
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def __prep_conf_matr(data):
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output_matrix = np.zeros([2, 2])
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for p, r in data:
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output_matrix[int(p)][int(r)] += 1
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return output_matrix
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def __plot_conf_matr(data):
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matr = __prep_conf_matr(data)
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_, ax = plt.subplots()
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ax.matshow(matr, cmap = plt.cm.Blues)
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for i, x in enumerate(matr):
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for j, y in enumerate(x):
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ax.text(i,
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j,
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str(round(y)),
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va = "center",
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ha = "center")
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plt.show()
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m.load_weights("model2.keras")
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tk = pickle.load(open('tokenizer.pickle', 'rb'))
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d = pd.read_csv("yelp_review_polarity_csv/test.csv", header = None, names = ['e', 'c'])
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c = d['c']
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e = d['e'] - 1
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c.progress_apply(fr)
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s = tk.texts_to_sequences(c)
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ps = kps(s, maxlen = 100)
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p = m.predict(ps, batch_size = 1024)
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data = [(i >= 0.5, v >= 0.5) for i, v in zip(p, e)]
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__plot_conf_matr(data)
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p = [i >= 0.5 for i in p]
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r = [i >= 0.5 for i in e]
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print(f"Accuracy : {accuracy_score(p, r)}")
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print(f"Precision : {precision_score(p, r, average = 'binary')}")
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print(f"Recall : {recall_score(p, r, average = 'binary')}")
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print(f"F1 Score : {f1_score(p, r, average = 'binary')}")
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