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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from datasets import load_dataset
from sklearn.manifold import TSNE
import streamlit as st
from clarin_datasets.dataset_to_show import DatasetToShow
from clarin_datasets.utils import embed_sentence, PLOT_COLOR_PALETTE
class CSTWikinewsDataset(DatasetToShow):
def __init__(self):
DatasetToShow.__init__(self)
self.dataset_name = "clarin-pl/cst-wikinews"
self.description = f"""
Dataset link: https://huggingface.co/datasets/{self.dataset_name}
"""
def load_data(self):
raw_dataset = load_dataset(self.dataset_name)
self.data_dict = {
subset: raw_dataset[subset].to_pandas() for subset in self.subsets
}
def show_dataset(self):
header = st.container()
dataframe_head = st.container()
class_distribution = st.container()
tsne_projection = st.container()
with header:
st.title(self.dataset_name)
with dataframe_head:
st.header("First 10 observations of the chosen subset")
subset_to_show = st.selectbox(
label="Select subset to see", options=self.subsets
)
df_to_show = self.data_dict[subset_to_show].head(10)
st.dataframe(df_to_show)
st.text_area(label="LaTeX code", value=df_to_show.style.to_latex())
class_distribution_df = pd.merge(
pd.DataFrame(
self.data_dict["train"]["label"]
.value_counts(normalize=True)
.reset_index(drop=False)
.rename({"index": "class"}, axis="columns")
),
pd.DataFrame(
self.data_dict["test"]["label"]
.value_counts(normalize=True)
.reset_index(drop=False)
.rename({"index": "class"}, axis="columns")
),
on="class",
).rename({"label_x": "train", "label_y": "test"}, axis="columns")
with class_distribution:
st.dataframe(class_distribution_df)
with tsne_projection:
st.header("t-SNE projection of the dataset")
subset_to_project = st.selectbox(
label="Select subset to project", options=self.subsets
)
first_sentences = self.data_dict[subset_to_project]["sentence_1"].values
second_sentences = self.data_dict[subset_to_project]["sentence_2"].values
labels = self.data_dict[subset_to_project]["label"].values
first_sentences_embedded = np.array([embed_sentence(x) for x in first_sentences])
second_sentences_embedded = np.array([embed_sentence(x) for x in second_sentences])
mean_embeddings = (first_sentences_embedded + second_sentences_embedded) / 2
reducer = TSNE(
n_components=2
)
transformed_embeddings = reducer.fit_transform(mean_embeddings)
fig, ax = plt.subplots()
ax.scatter(
x=transformed_embeddings[:, 0],
y=transformed_embeddings[:, 1],
c=[
PLOT_COLOR_PALETTE[i] for i in labels
]
)
st.pyplot(fig)
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