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| """ | |
| @author: Tan Quang Duong | |
| """ | |
| import torch | |
| import matplotlib | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| import numpy as np | |
| import pandas as pd | |
| from sklearn.metrics import classification_report | |
| # custom color map | |
| norm = matplotlib.colors.Normalize(-1, 1) | |
| colors = [[norm(-1.0), "#DAF7A6"], [norm(1.0), "#673FEE"]] | |
| custom_cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", colors) | |
| def create_classification_report(y, y_pred): | |
| target_class = ["negative", "positive"] | |
| cls_report = classification_report( | |
| y, y_pred, target_names=target_class, output_dict=True | |
| ) | |
| df_report = pd.DataFrame(cls_report).transpose() | |
| return df_report.round(2) | |
| def get_100_random_test_review(df_test): | |
| # get random 100 reviews | |
| n_random = np.random.randint(len(df_test) - 101) | |
| # get dataframe of 100 reviews | |
| df_test_100 = df_test.iloc[n_random : n_random + 100] | |
| # column rename | |
| df_test_100 = df_test_100.rename(columns={"label": "class_id"}) | |
| return df_test_100 | |
| def inference_from_pytorch(text, tokenizer, model): | |
| inputs = tokenizer(text, return_tensors="pt", truncation=True) | |
| # do inference | |
| with torch.no_grad(): | |
| logits = model(**inputs).logits | |
| # get label | |
| predicted_class_id = logits.argmax().item() | |
| predicted_label = model.config.id2label[predicted_class_id] | |
| return predicted_class_id, predicted_label | |
| def plot_confusion_matric(confusion_matrix): | |
| # annot=True to annotate cells, ftm='g' to disable scientific notation | |
| sentiment_labels = ["Negative", "Positive"] | |
| fig_cm, ax = plt.subplots(figsize=(8, 8)) | |
| sns.heatmap( | |
| confusion_matrix, | |
| annot=True, | |
| fmt="g", | |
| cmap=custom_cmap, | |
| ax=ax, | |
| ) | |
| # labels, title and ticks | |
| ax.set_xlabel("Predicted labels", size=12, weight="bold") | |
| ax.set_ylabel("True labels", size=12, weight="bold") | |
| ax.set_title("Confusion matrix", size=16, weight="bold") | |
| ax.xaxis.set_ticklabels(sentiment_labels) | |
| ax.yaxis.set_ticklabels(sentiment_labels) | |
| return fig_cm | |
| def plot_donut_sentiment_percentage(df): | |
| # explosion | |
| explode_val = (0.05, 0.05) | |
| custom_colors = ["#673FEE", "#DAF7A6"] | |
| # Give color names | |
| fig_pie, ax_pie = plt.subplots() | |
| ax_pie.pie( | |
| df["count"], | |
| labels=df["label"], | |
| autopct="%1.1f%%", | |
| pctdistance=0.5, | |
| explode=explode_val, | |
| colors=custom_colors, | |
| ) | |
| ax_pie.set_title("Sentiment analysis", size=12, weight="bold") | |
| # Create a circle at the center of the plot | |
| my_circle = plt.Circle((0, 0), 0.7, color="white") | |
| p = plt.gcf() | |
| p.gca().add_artist(my_circle) | |
| return fig_pie | |