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| import numpy as np | |
| import plotly.graph_objects as go | |
| import streamlit as st | |
| from .lib import set_input | |
| def get_w(f, ec=0.86, rv=0.50): | |
| result = (rv + f-1)/(ec + f-1) | |
| result = np.clip(result, 0, 1) | |
| # print(f'w = {result}') | |
| return result | |
| def get_f(w, ec=0.86, rv=0.50): | |
| result = 1+(ec*w-rv)/(1-w) | |
| result = np.clip(result, 0, 1) | |
| # print(f'f = {result}') | |
| return result | |
| def get_pe(w, ec=0.86, f=0.50): | |
| result = ec*w+(1-w)*(1-f) | |
| result = np.clip(result, 0, 1) | |
| # print(f'f = {result}') | |
| return result | |
| def get_pe2(w, ec=0.86, f=0.50): | |
| result = 1-np.exp(-w*ec/(f+0.0001)) | |
| result = result | |
| # result = np.clip(result, 0, 1) | |
| # print(f'f = {result}') | |
| return result | |
| def get_pe3(w, ec=0.86, f=0.50): | |
| result_np = np.zeros((len(f), len(f))) | |
| st.markdown(result_np) | |
| st.markdown(result_np[0]) | |
| for i, fi in enumerate(f): | |
| result = 1-np.exp(-ec/f) | |
| result = w*result | |
| if i ==0: | |
| result_np[i]=w*result | |
| else: | |
| result_np[i]=w*result + (1-w)*result_np[i-1] | |
| # result = np.clip(result, 0, 1) | |
| # print(f'f = {result}') | |
| return result | |
| xdata1 = np.arange(0, 1.1, step=0.01) | |
| # rand = np.random.random_sample() | |
| def proper_float(i): | |
| return np.round(i, 2) | |
| def get_text(): | |
| return ''' | |
| ### Eric's proposal | |
| > I would propose a scoring metric something like this: | |
| > * `probability_emote = w * emotion_confidence + (1 - w) * frequency_penalty` | |
| > | |
| > Where: | |
| > * emotion_confidence: a score from 0.0 to 1.0 representing the emotion model’s confidence in it’s classification results | |
| > * frequency_penalty: a score from 0.0 to 1.0 where a high score penalizes frequent emotes | |
| > * `frequency_penalty = 1 - emotion_frequency` | |
| > * w: a weight from 0.0 to 1.0 that controls the balance between emotion_confidence and frequency_penalty | |
| > * Then you generate a random number between 0.0 and 1.0 and emote if it is greater than probability_emote | |
| > * You will have to set frequency_penalty and w through trial and error, but you can start with setting w=0.5 and giving the emotion classifier and frequency penalty equal weight. | |
| > * Setting w=1.0 would disable the frequency penalty altogether | |
| ''' | |
| def get_equation_text(w=0.5, ec=0.7, rand=None, emotion_frequency=None, graph_eq='PE1'): | |
| if graph_eq=='PE1': | |
| text = f''' | |
| #### Equation | |
| ``` | |
| frequency_penalty = 1 - emotion_frequency | |
| probability_emote = w * emotion_confidence + (1 - w) * frequency_penalty | |
| ``` | |
| **probability_emote** = {proper_float(w)} * {proper_float(ec)} + {proper_float(1-w)} * frequency_penalty | |
| ''' | |
| if rand is not None: | |
| frequency_penalty = proper_float(1-emotion_frequency) | |
| probability_emote = proper_float((w)*(ec)+(1-w)*frequency_penalty) | |
| text = f''' | |
| #### Equation | |
| ``` | |
| frequency_penalty = 1 - emotion_frequency = 1 - {proper_float(emotion_frequency)} = {frequency_penalty} | |
| probability_emote = w * emotion_confidence + (1 - w) * frequency_penalty | |
| probability_emote = {proper_float(w)} * {proper_float(ec)} + {proper_float(1-w)} * {frequency_penalty} | |
| ``` | |
| **probability_emote** = {probability_emote} | |
| ``` | |
| Show_Emotion | |
| = probability_emote > (Random value between 0 and 1) | |
| Random value = {rand} | |
| Show_Emotion = {probability_emote} > {rand} | |
| ``` | |
| **Show_Emotion** = {probability_emote > rand} | |
| ''' | |
| elif graph_eq=='PE2' or graph_eq=='PE3': | |
| # probability_emote = 1 - e ^ (frequency / emotion_confidence) | |
| text = f''' | |
| #### Equation | |
| ``` | |
| probability_emote = 1 - e ^ (- w * emotion_confidence / frequency) | |
| ``` | |
| **probability_emote** = 1 - e ^ (- {proper_float(w)} * {proper_float(ec)} / frequency) | |
| ''' | |
| return text | |
| def run_probability_emote(container_param): | |
| graph_eq = container_param.selectbox('Select Equation', ['PE1', 'PE2', 'PE3']) | |
| w = set_input(container_param, | |
| label='Weight w', key_slider='w_slider', key_input='w_input', | |
| min_value=0., | |
| max_value=1., | |
| value=.5, | |
| step=.01,) | |
| score = set_input(container_param, | |
| label='Confidence Score', key_slider='score_slider', key_input='score_input', | |
| min_value=0., | |
| max_value=1., | |
| value=.5, | |
| step=.01,) | |
| # score = container_param.slider( | |
| # label='Confidence Score', | |
| # min_value=0., | |
| # max_value=1., | |
| # value=.5, | |
| # step=.01) | |
| calculate_check = container_param.checkbox(label='Calculate', value=False) | |
| if calculate_check: | |
| emotion_frequency = set_input(container_param, | |
| label='Emotion Frequency', key_slider='emotion_frequency_slider_slider', key_input='emotion_frequency_slider_input', | |
| min_value=0., | |
| max_value=1., | |
| value=.5, | |
| step=.01,) | |
| rand = set_input(container_param, | |
| label='Random Value', key_slider='rand_slider', key_input='rand_input', | |
| min_value=0., | |
| max_value=1., | |
| value=.5, | |
| step=.01,) | |
| else: | |
| emotion_frequency = 'emotion_frequency' | |
| rand = None | |
| st.markdown(get_equation_text(w=w, ec=score, rand=rand, | |
| emotion_frequency=emotion_frequency, graph_eq=graph_eq)) | |
| fig = go.Figure() | |
| # fig.add_trace(go.Scatter(x=xdata1, y=np.ones_like(xdata1)*rand, | |
| # mode='markers', name='Random', | |
| # line=dict(color='#ff8300', width=2) | |
| # )) | |
| if calculate_check: | |
| dd = 0.01 | |
| fig.add_hline(y=rand, line_width=3, | |
| line_dash="dash", line_color="#ff8300") | |
| fig.add_vline(x=emotion_frequency, line_width=3, | |
| line_dash="dash", line_color="green") | |
| fig.add_trace(go.Scatter( | |
| x=[emotion_frequency-dd, emotion_frequency+dd], y=[rand-dd, rand+dd], | |
| mode='lines', | |
| line=dict(color='#ee00ee', width=8) | |
| ),) | |
| fig.add_trace(go.Scatter( | |
| x=[emotion_frequency+dd, emotion_frequency-dd], y=[rand-dd, rand+dd], | |
| mode='lines', | |
| line=dict(color='#ee00ee', width=8) | |
| ),) | |
| if graph_eq=='PE1': get_result = get_pe | |
| elif graph_eq=='PE2': get_result = get_pe2 | |
| elif graph_eq=='PE3': get_result = get_pe3 | |
| fig.add_trace(go.Scatter( | |
| x=xdata1, y=get_result(w=w, f=xdata1, ec=score), | |
| mode='lines', | |
| name='Probability-Emote', | |
| line=dict(color='#00eeee', width=4) | |
| ), | |
| ) | |
| fig.update_layout( | |
| template='plotly_dark', | |
| xaxis_range=[0., 1.], | |
| yaxis_range=[0., 1.], | |
| xaxis_title="Emotion Frequency", | |
| yaxis_title="Probability Emote", | |
| showlegend=False | |
| ) | |
| st.plotly_chart(fig, use_container_width=True) | |
| st.markdown(get_text()) | |