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RuBert-tiny2-EmotionsDetected

Text Classification
Safetensors
Russian
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RuBert-tiny2-EmotionsDetected
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import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

def plot_emotion_confusion_matrix(results_df, emotion_columns):

    correct_count = {emotion: 0 for emotion in emotion_columns}
    incorrect_count = {emotion: 0 for emotion in emotion_columns}
    undefined_count = {emotion: 0 for emotion in emotion_columns}

    for idx, row in results_df.iterrows():
        true_emotions = set(row['true emotions'].split()) if isinstance(row['true emotions'], str) else set()
        predicted_emotions = set(row['predict emotions'].split()) if isinstance(row['predict emotions'], str) else set()

        for emotion in emotion_columns:
            if emotion in true_emotions and emotion in predicted_emotions:
                correct_count[emotion] += 1
            elif emotion in predicted_emotions and emotion not in true_emotions:
                incorrect_count[emotion] += 1
            elif emotion in true_emotions and emotion not in predicted_emotions:
                undefined_count[emotion] += 1

    data = []
    for emotion in emotion_columns:
        data.append([
            correct_count[emotion],
            incorrect_count[emotion],
            undefined_count[emotion]
        ])
    
    heatmap_df = pd.DataFrame(data, columns=["Correctly Identified", "Incorrectly Identified", "Undefined"], index=emotion_columns)

    num_examples = len(results_df)

    plt.figure(figsize=(10, 12))
    sns.heatmap(heatmap_df, annot=True, cmap="Blues", fmt="d", cbar=False)
    plt.title(f"Emotion Prediction Confusion Matrix (Examples: {num_examples})")
    plt.xlabel("Prediction Status")
    plt.ylabel("Emotion")
    plt.xticks(rotation=45, ha="right")
    plt.tight_layout()
    plt.show()


def plot_true_emotion_frequency(results_df, emotion_columns):

    true_emotion_count = {emotion: 0 for emotion in emotion_columns}

    for idx, row in results_df.iterrows():
        true_emotions = set(row['true emotions'].split()) if isinstance(row['true emotions'], str) else set()

        for emotion in emotion_columns:
            if emotion in true_emotions:
                true_emotion_count[emotion] += 1

    data = []
    for emotion in emotion_columns:
        data.append([true_emotion_count[emotion]])

    heatmap_df = pd.DataFrame(data, columns=["True Emotion Count"], index=emotion_columns)

    plt.figure(figsize=(10, 12))
    sns.heatmap(heatmap_df, annot=True, cmap="YlGnBu", fmt="d", cbar=False)
    plt.title(f"True Emotion Frequency (Examples: {len(results_df)})")
    plt.xlabel("True Emotion Count")
    plt.ylabel("Emotion")
    plt.xticks(rotation=45, ha="right")
    plt.tight_layout()
    plt.show()


def plot_predicted_emotion_frequency(results_df, emotion_columns):
    
    predicted_emotion_count = {emotion: 0 for emotion in emotion_columns}

    for idx, row in results_df.iterrows():
        predicted_emotions = set(row['predict emotions'].split()) if isinstance(row['predict emotions'], str) else set()

        for emotion in emotion_columns:
            if emotion in predicted_emotions:
                predicted_emotion_count[emotion] += 1

    data = []
    for emotion in emotion_columns:
        data.append([predicted_emotion_count[emotion]])

    heatmap_df = pd.DataFrame(data, columns=["Predicted Emotion Count"], index=emotion_columns)

    plt.figure(figsize=(10, 12))
    sns.heatmap(heatmap_df, annot=True, cmap="YlOrRd", fmt="d", cbar=False)
    plt.title(f"Predicted Emotion Frequency (Examples: {len(results_df)})")
    plt.xlabel("Predicted Emotion Count")
    plt.ylabel("Emotion")
    plt.xticks(rotation=45, ha="right")
    plt.tight_layout()
    plt.show()


csv_path = "RuBert-tiny2-EmotionsDetected/Dstasets/Emotions_detected.csv" 
results_df = pd.read_csv(csv_path)

emotion_columns = [
    "admiration", "amusement", "anger", "annoyance", "approval", "caring", "confusion", "curiosity", "desire",
    "disappointment", "disapproval", "disgust", "embarrassment", "excitement", "fear", "gratitude", "grief", "joy",
    "love", "nervousness", "optimism", "pride", "realization", "relief", "remorse", "sadness", "surprise", "neutral"
]

plot_true_emotion_frequency(results_df, emotion_columns)
plot_predicted_emotion_frequency(results_df, emotion_columns)
plot_emotion_confusion_matrix(results_df, emotion_columns)