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Browse files- backend/controller.py +1 -0
- explanation/interpret.py +53 -19
- explanation/visualize.py +23 -44
- main.py +5 -7
- public/credits_dataprotection_license.md +1 -0
backend/controller.py
CHANGED
@@ -10,6 +10,7 @@ from explanation import interpret, visualize
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# main interference function that that calls chat functions depending on selections
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def interference(
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prompt: str,
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history: list,
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# main interference function that that calls chat functions depending on selections
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# TODO: Limit maximum tokens/model input
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def interference(
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prompt: str,
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history: list,
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explanation/interpret.py
CHANGED
@@ -3,11 +3,16 @@
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import seaborn as sns
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import matplotlib.pyplot as plt
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import numpy as np
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from shap import plots, PartitionExplainer
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# internal imports
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from utils import formatting as fmt
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# main explain function that returns a chat with explanations
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def chat_explained(model, prompt):
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return response_text, graphic, plot
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# graphic plotting function that creates a html graphic (as string) for the explanation
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def create_graphic(shap_values):
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# create the html graphic using shap text plot function
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@@ -42,21 +68,29 @@ def create_plot(shap_values):
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output_names = shap_values.output_names
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input_names = shap_values.data[0]
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# Set seaborn style to dark
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sns.set(style="
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fig, ax = plt.subplots()
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# Setting figure size
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fig.set_size_inches(
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max(
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max(
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)
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# Plotting the heatmap with Seaborn's color palette
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im = ax.imshow(
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-
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vmax=
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vmin
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cmap=sns.color_palette("vlag_r", as_cmap=True),
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aspect="auto",
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)
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@@ -64,25 +98,25 @@ def create_plot(shap_values):
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# Creating colorbar
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cbar = ax.figure.colorbar(im, ax=ax)
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cbar.ax.set_ylabel("Token Attribution", rotation=-90, va="bottom")
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cbar.ax.yaxis.set_tick_params(color="
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plt.setp(plt.getp(cbar.ax.axes, "yticklabels"), color="
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# Setting ticks and labels with white color for visibility
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ax.
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ax.
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plt.setp(ax.get_xticklabels(), color="
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plt.setp(ax.get_yticklabels(), color="
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# Adjusting tick labels
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ax.tick_params(
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top=True, bottom=False, labeltop=False, labelbottom=True, color="white"
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)
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# Adding text annotations
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for i in range(
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return plt
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import seaborn as sns
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import matplotlib.pyplot as plt
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import numpy as np
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from shap import models, maskers, plots, PartitionExplainer
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import torch
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# internal imports
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from utils import formatting as fmt
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# global variables
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TEACHER_FORCING = None
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TEXT_MASKER = None
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# main explain function that returns a chat with explanations
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def chat_explained(model, prompt):
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return response_text, graphic, plot
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def wrap_shap(model):
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global TEXT_MASKER, TEACHER_FORCING
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# set the device to cuda if gpu is available
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# updating the model settings again
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model.set_config()
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# (re)initialize the shap models and masker
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text_generation = models.TextGeneration(model.MODEL, model.TOKENIZER)
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TEACHER_FORCING = models.TeacherForcing(
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text_generation,
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model.TOKENIZER,
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device=str(device),
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similarity_model=model.MODEL,
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similarity_tokenizer=model.TOKENIZER,
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)
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TEXT_MASKER = maskers.Text(model.TOKENIZER, " ", collapse_mask_token=True)
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# graphic plotting function that creates a html graphic (as string) for the explanation
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def create_graphic(shap_values):
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# create the html graphic using shap text plot function
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output_names = shap_values.output_names
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input_names = shap_values.data[0]
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# Transpose the values for horizontal input names
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transposed_values = np.transpose(values)
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# Set seaborn style to dark
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sns.set(style="dark")
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fig, ax = plt.subplots()
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# Making background transparent
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ax.set_alpha(0)
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fig.patch.set_alpha(0)
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# Setting figure size
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fig.set_size_inches(
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max(transposed_values.shape[1] * 2, 10),
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max(transposed_values.shape[0] / 1.5, 5),
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)
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# Plotting the heatmap with Seaborn's color palette
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im = ax.imshow(
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transposed_values,
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vmax=transposed_values.max(),
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vmin=-transposed_values.min(),
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cmap=sns.color_palette("vlag_r", as_cmap=True),
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aspect="auto",
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)
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# Creating colorbar
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cbar = ax.figure.colorbar(im, ax=ax)
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cbar.ax.set_ylabel("Token Attribution", rotation=-90, va="bottom")
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cbar.ax.yaxis.set_tick_params(color="white")
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plt.setp(plt.getp(cbar.ax.axes, "yticklabels"), color="white")
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# Setting ticks and labels with white color for visibility
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ax.set_xticks(np.arange(len(input_names)), labels=input_names)
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ax.set_yticks(np.arange(len(output_names)), labels=output_names)
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plt.setp(ax.get_xticklabels(), color="white", rotation=45, ha="right")
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plt.setp(ax.get_yticklabels(), color="white")
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# Adjusting tick labels
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ax.tick_params(
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top=True, bottom=False, labeltop=False, labelbottom=True, color="white"
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)
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# Adding text annotations - not used for readability
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# for i in range(transposed_values.shape[0]):
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# for j in range(transposed_values.shape[1]):
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# val = transposed_values[i, j]
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# color = "black" if 0.2 < im.norm(val) < 0.8 else "white"
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# ax.text(j, i, f"{val:.4f}", ha="center", va="center", color=color)
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return plt
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explanation/visualize.py
CHANGED
@@ -13,7 +13,6 @@ from utils import formatting as fmt
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# plotting function that plots the attention values in a heatmap
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def chat_explained(model, prompt):
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# reset the model config to default
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model.set_config()
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# get encoded input and output vectors
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prompt, return_tensors="pt", add_special_tokens=True
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).input_ids
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decoder_input_ids = model.MODEL.generate(encoder_input_ids, output_attentions=True)
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# get the text for the input and output vectors
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encoder_text = fmt.format_tokens(
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model.TOKENIZER.convert_ids_to_tokens(encoder_input_ids[0])
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)
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# create the response text, graphic and plot
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response_text = fmt.format_output_text(decoder_text)
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graphic = create_graphic(attention_output, (encoder_text, decoder_text))
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graphic = (
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'<div style="text-align: center"><h4>Interactive Graphic for Attention'
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" currently WIP</h4></div>"
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)
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plot = create_plot(attention_output, (encoder_text, decoder_text))
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return
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response_text,
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graphic,
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plot,
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)
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# creating a html graphic using BERTViz
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# TODO: FIX
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def create_graphic(attention_output, enc_dec_texts: tuple):
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# calls the head_view function of BERTViz to return html graphic
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# creating an attention heatmap plot using seaborn
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# CREDIT: adopted from official Matplotlib documentation
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## see https://matplotlib.org/stable/
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def create_plot(attention_output, enc_dec_texts: tuple):
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# get the averaged attention weights
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attention = attention_output.cross_attentions[0][0].detach().numpy()
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averaged_attention_weights = np.mean(attention, axis=0)
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averaged_attention_weights = np.transpose(averaged_attention_weights)
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# get the encoder and decoder tokens
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encoder_tokens = enc_dec_texts[0]
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decoder_tokens = enc_dec_texts[1]
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# set seaborn style to dark and initialize figure and axis
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sns.set(style="
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fig, ax = plt.subplots()
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# Setting figure size
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fig.set_size_inches(
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max(averaged_attention_weights.shape[1] * 2, 10),
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max(averaged_attention_weights.shape[0]
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)
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# Plotting the heatmap with seaborn's color palette
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# Creating colorbar
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cbar = ax.figure.colorbar(im, ax=ax)
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cbar.ax.set_ylabel("
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cbar.ax.yaxis.set_tick_params(color="
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plt.setp(plt.getp(cbar.ax.axes, "yticklabels"), color="
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# Setting ticks and labels with
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ax.
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ax.
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ax.
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plt.setp(ax.
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color = (
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"white"
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if im.norm(averaged_attention_weights.max()) / 2 > im.norm(val)
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else "black"
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)
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ax.text(j, i, f"{val:.4f}", ha="center", va="center", color=color)
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# return the plot
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return plt
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# plotting function that plots the attention values in a heatmap
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def chat_explained(model, prompt):
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model.set_config()
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# get encoded input and output vectors
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prompt, return_tensors="pt", add_special_tokens=True
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).input_ids
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decoder_input_ids = model.MODEL.generate(encoder_input_ids, output_attentions=True)
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encoder_text = fmt.format_tokens(
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model.TOKENIZER.convert_ids_to_tokens(encoder_input_ids[0])
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)
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# create the response text, graphic and plot
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response_text = fmt.format_output_text(decoder_text)
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graphic = create_graphic(attention_output, (encoder_text, decoder_text))
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plot = create_plot(attention_output, (encoder_text, decoder_text))
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return response_text, graphic, plot
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# creating a html graphic using BERTViz
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def create_graphic(attention_output, enc_dec_texts: tuple):
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# calls the head_view function of BERTViz to return html graphic
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# creating an attention heatmap plot using seaborn
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def create_plot(attention_output, enc_dec_texts: tuple):
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# get the averaged attention weights
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attention = attention_output.cross_attentions[0][0].detach().numpy()
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averaged_attention_weights = np.mean(attention, axis=0)
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# get the encoder and decoder tokens
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encoder_tokens = enc_dec_texts[0]
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decoder_tokens = enc_dec_texts[1]
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# set seaborn style to dark and initialize figure and axis
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sns.set(style="dark")
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fig, ax = plt.subplots()
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# Making background transparent
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ax.set_alpha(0)
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fig.patch.set_alpha(0)
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# Setting figure size
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fig.set_size_inches(
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max(averaged_attention_weights.shape[1] * 2, 10),
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max(averaged_attention_weights.shape[0] / 1.5, 5),
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)
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# Plotting the heatmap with seaborn's color palette
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# Creating colorbar
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cbar = ax.figure.colorbar(im, ax=ax)
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cbar.ax.set_ylabel("Token Attribution", rotation=-90, va="bottom")
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cbar.ax.yaxis.set_tick_params(color="white")
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plt.setp(plt.getp(cbar.ax.axes, "yticklabels"), color="white")
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# Setting ticks and labels with white color for visibility
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ax.set_xticks(np.arange(len(encoder_tokens)), labels=encoder_tokens)
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ax.set_yticks(np.arange(len(decoder_tokens)), labels=decoder_tokens)
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plt.setp(ax.get_xticklabels(), color="white", rotation=45, ha="right")
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plt.setp(ax.get_yticklabels(), color="white")
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# Adjusting tick labels
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ax.tick_params(
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top=True, bottom=False, labeltop=False, labelbottom=True, color="white"
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)
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return plt
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main.py
CHANGED
@@ -187,7 +187,7 @@ with gr.Blocks(
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Values have been excluded for readability. See colorbar for value indication.
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""")
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# plot component that takes a matplotlib figure as input
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xai_plot = gr.Plot(label="Token Level Explanation")
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# functions to trigger the controller
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## takes information for the chat and the xai selection
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[user_prompt, chatbot, xai_interactive, xai_plot],
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)
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# final row to
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##
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with gr.Tab(label="
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gr.Markdown(value=load_md("public/
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with gr.Accordion(label="Credits, Data Protection and License", open=False):
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gr.Markdown(value=load_md("public/credits_dataprotection_license.md"))
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# mount function for fastAPI Application
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app = gr.mount_gradio_app(app, ui, path="/")
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Values have been excluded for readability. See colorbar for value indication.
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""")
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# plot component that takes a matplotlib figure as input
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xai_plot = gr.Plot(label="Token Level Explanation", scale=3)
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# functions to trigger the controller
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## takes information for the chat and the xai selection
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[user_prompt, chatbot, xai_interactive, xai_plot],
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)
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# final row to show legal information
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## - credits, data protection and link to the License
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with gr.Tab(label="Credits, Data Protection and License"):
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gr.Markdown(value=load_md("public/credits_dataprotection_license.md"))
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# mount function for fastAPI Application
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app = gr.mount_gradio_app(app, ui, path="/")
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public/credits_dataprotection_license.md
CHANGED
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# Credits
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### Models
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This implementation is build on GODEL by Microsoft, Inc.
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# Credits
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For full credits, please refer to the [thesis print]()
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### Models
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This implementation is build on GODEL by Microsoft, Inc.
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