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import streamlit as st |
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import os |
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import base64 |
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import json |
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import pandas as pd |
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pd.options.mode.chained_assignment = None |
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import numpy as np |
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import matplotlib.pyplot as plt |
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class Visualization: |
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def __init__( |
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self, |
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path_instructions, |
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path_data, |
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lang, |
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num_docs, |
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num_docs_for_words, |
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max_len_text_display, |
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): |
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self.path_instructions = path_instructions |
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self.path_data = path_data |
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self.lang = lang |
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self.num_docs = num_docs |
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self.num_docs_for_words = num_docs_for_words |
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self.max_len_text_display = max_len_text_display |
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def preamble(self): |
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st.markdown( |
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"Before diving into this demo, you might want to take a look at how the filtering pipeline looks like in more detail." |
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) |
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def get_binary_file_downloader_html(bin_file, file_label="File"): |
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with open(bin_file, "rb") as f: |
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data = f.read() |
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bin_str = base64.b64encode(data).decode() |
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href = f'<a href="data:application/octet-stream;base64,{bin_str}" download="{os.path.basename(bin_file)}">{file_label}</a>' |
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return href |
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st.markdown( |
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get_binary_file_downloader_html( |
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self.path_instructions, |
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"Download the explanation of the filtering pipeline as pdf", |
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), |
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unsafe_allow_html=True, |
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) |
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def open_data(self): |
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with open(self.path_data) as json_file: |
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data = json.load(json_file) |
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self.num_docs = min(self.num_docs, len(data)) |
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self.num_docs_for_words = min(self.num_docs_for_words, len(data)) |
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if "words" in data[0]: |
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words = [doc["words"] for doc in data[: self.num_docs_for_words]] |
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words = [word for doc in words for word in doc] |
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self.words = pd.DataFrame(words) |
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else: |
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self.words = None |
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docs = data[: self.num_docs] |
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for doc in docs: |
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if not (self.words is None): |
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del doc["words"] |
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if len(doc["text"]) > self.max_len_text_display: |
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doc["text"] = ( |
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doc["text"][: self.max_len_text_display] |
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+ " [...] [THIS LONG TEXT HAS BEEN TRUNCATED FOR DISPLAY REASONS]" |
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) |
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self.docs_checkpoint = pd.DataFrame(docs) |
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self.docs = self.docs_checkpoint |
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def set_title(self): |
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st.title(f"{self.num_docs} {self.lang} documents with their stats.") |
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def filtering_of_docs(self): |
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st.sidebar.subheader("Parameters of the filtering on documents") |
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def set_sliders(): |
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columns = list(self.docs) |
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keys = [] |
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conds = {} |
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def get_cond(key, cutoff, max_cutoff): |
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if max_cutoff: |
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return self.docs[key] <= cutoff |
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return self.docs[key] >= cutoff |
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def print_discared_by_cond(cond): |
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st.sidebar.caption( |
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f"{(len(cond) - np.sum(1*cond)) / len(cond) * 100:.2f}% of the total is discarded with this filter." |
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) |
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st.sidebar.caption("---------") |
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if "number_words" in columns: |
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cutoff_def = "If the number of words of a document is lower than this number, the document is removed." |
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max_nb_words = int(np.max(self.docs["number_words"])) + 1 |
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cutoff_min_number_words = st.sidebar.slider( |
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cutoff_def, 0, min(max_nb_words, 500), 0 |
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) |
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new_key = ("number_words", cutoff_min_number_words, False) |
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keys.append(new_key) |
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cond_1 = get_cond(new_key[0], new_key[1], new_key[2]) |
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print_discared_by_cond(cond_1) |
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cutoff_def = "If the number of words of a document is higher than this number, the document is removed." |
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cutoff_max_number_words = st.sidebar.slider( |
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cutoff_def, 0, max_nb_words, max_nb_words |
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) |
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new_key = ("number_words", cutoff_max_number_words, True) |
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keys.append(new_key) |
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cond_2 = get_cond(new_key[0], new_key[1], new_key[2]) |
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print_discared_by_cond(cond_2) |
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conds["number_words"] = [cond_1, cond_2] |
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if "repetitions_ratio" in columns: |
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val_repetitions_lengths = list( |
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self.docs["repetitions_ratio"].iloc[0].keys() |
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) |
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default_index = ( |
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val_repetitions_lengths.index("10") |
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if "10" in val_repetitions_lengths |
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else 0 |
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) |
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label_selectbox = ( |
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"Length of the repetitions (that will determine the repetitions ratio). " |
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"Choosing a higher or lower number does not mean that the filtering " |
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"is stronger or weaker. Be careful, choosing a low number (below 5 for languages like English) " |
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"tends to associate a high repetitions ratio to very long documents (like book chapters), but with " |
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"few or no repetitions, simply because their length gives them more diversity, and we do " |
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"not want to discard such documents." |
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) |
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repetitions_length = st.sidebar.selectbox( |
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label=label_selectbox, |
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options=val_repetitions_lengths, |
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index=default_index, |
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) |
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self.docs = self.docs_checkpoint |
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for i in range(len(self.docs["repetitions_ratio"])): |
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self.docs["repetitions_ratio"].iloc[i] = self.docs["repetitions_ratio"].iloc[i][repetitions_length] |
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cutoff_def = "If the repetitions ratio of a document is higher than this number, the document is removed." |
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cutoff_repetitions_ratio = st.sidebar.slider( |
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cutoff_def, 0.0, 1.0, 1.0, step=0.01 |
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) |
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new_key = ( |
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"repetitions_ratio", |
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cutoff_repetitions_ratio, |
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True, |
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) |
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keys.append(new_key) |
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cond = get_cond(new_key[0], new_key[1], new_key[2]) |
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print_discared_by_cond(cond) |
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conds["repetitions_ratio"] = [cond] |
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if "special_characters_ratio" in columns: |
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cutoff_def = "If the special characters ratio of a document is higher than this number, the document is removed." |
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cutoff_special_characters_ratio = st.sidebar.slider( |
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cutoff_def, 0.0, 1.0, 1.0, step=0.01 |
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) |
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new_key = ( |
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"special_characters_ratio", |
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cutoff_special_characters_ratio, |
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True, |
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) |
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keys.append(new_key) |
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cond = get_cond(new_key[0], new_key[1], new_key[2]) |
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print_discared_by_cond(cond) |
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conds["special_characters_ratio"] = [cond] |
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if "stopwords_ratio" in columns: |
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cutoff_def = "If the stop words ratio of a document is lower than this number, the document is removed." |
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cutoff_stopwords_ratio = st.sidebar.slider( |
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cutoff_def, 0.0, 1.0, 0.0, step=0.01 |
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) |
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new_key = ("stopwords_ratio", cutoff_stopwords_ratio, False) |
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keys.append(new_key) |
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cond = get_cond(new_key[0], new_key[1], new_key[2]) |
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print_discared_by_cond(cond) |
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conds["stopwords_ratio"] = [cond] |
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if "badwords_ratio" in columns: |
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cutoff_def = "If the bad words ratio of a document is higher than this number, the document is removed." |
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cutoff_badwords_ratio = st.sidebar.slider( |
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cutoff_def, 0.0, 1.0, 1.0, step=0.01 |
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) |
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new_key = ("badwords_ratio", cutoff_badwords_ratio, True) |
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keys.append(new_key) |
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cond = get_cond(new_key[0], new_key[1], new_key[2]) |
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print_discared_by_cond(cond) |
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conds["badwords_ratio"] = [cond] |
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if "lang_id_score" in columns: |
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cutoff_def = "If the confidence score for the language identification prediction of a document is lower than this number, the document is removed." |
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cutoff_lang_id_score = st.sidebar.slider( |
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cutoff_def, 0.0, 1.0, 0.0, step=0.01 |
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) |
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new_key = ("lang_id_score", cutoff_lang_id_score, False) |
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keys.append(new_key) |
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cond = get_cond(new_key[0], new_key[1], new_key[2]) |
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print_discared_by_cond(cond) |
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conds["lang_id_score"] = [cond] |
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if "perplexity_score" in columns: |
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cutoff_def = "If the perplexity score of a document is higher than this number, the document is removed." |
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max_pp = int(np.max(self.docs["perplexity_score"])) + 1 |
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cutoff_perplexity_score = st.sidebar.slider( |
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cutoff_def, 0, max_pp, max_pp |
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) |
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new_key = ("perplexity_score", cutoff_perplexity_score, True) |
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keys.append(new_key) |
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cond = get_cond(new_key[0], new_key[1], new_key[2]) |
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print_discared_by_cond(cond) |
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conds["perplexity_score"] = [cond] |
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return keys, conds |
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self.keys, conds = set_sliders() |
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all_conds = [subcond for cond in list(conds.values()) for subcond in cond] |
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all_conds = np.all(all_conds, axis=0) |
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st.header("Filtering on documents") |
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def display_dataset(cond, description): |
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displayed_docs = self.docs.loc[cond] |
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st.subheader( |
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f"{description}: {len(displayed_docs)} docs ({len(displayed_docs) / self.num_docs * 100:.2f}%)" |
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) |
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st.markdown( |
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"Click on a column to sort by it, place the cursor on the text to display it." |
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) |
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st.dataframe(displayed_docs) |
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display_dataset(np.invert(all_conds), "Discarded documents") |
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display_discarded_documents_by_filter = st.checkbox( |
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"Display discarded documents by filter" |
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) |
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if display_discarded_documents_by_filter: |
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columns = list(self.docs) |
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if "number_words" in columns: |
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cond_filter = np.invert(np.all(conds["number_words"], axis=0)) |
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display_dataset( |
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cond_filter, |
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"Discarded documents for the filter on the number of words", |
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) |
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if "repetitions_ratio" in columns: |
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cond_filter = np.invert(np.all(conds["repetitions_ratio"], axis=0)) |
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display_dataset( |
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cond_filter, |
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"Discarded documents for the filter on the repetitions ratio", |
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) |
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if "special_characters_ratio" in columns: |
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cond_filter = np.invert( |
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np.all(conds["special_characters_ratio"], axis=0) |
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) |
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display_dataset( |
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cond_filter, |
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"Discarded documents for the filter on the special characters ratio", |
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) |
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if "stopwords_ratio" in columns: |
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cond_filter = np.invert(np.all(conds["stopwords_ratio"], axis=0)) |
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display_dataset( |
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cond_filter, |
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"Discarded documents for the filter on the stop words ratio", |
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) |
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if "badwords_ratio" in columns: |
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cond_filter = np.invert(np.all(conds["badwords_ratio"], axis=0)) |
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display_dataset( |
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cond_filter, |
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"Discarded documents for the filter on the bad words ratio", |
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) |
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if "lang_id_score" in columns: |
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cond_filter = np.invert(np.all(conds["lang_id_score"], axis=0)) |
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display_dataset( |
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cond_filter, |
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"Discarded documents for the filter on the language identification confidence score", |
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) |
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if "perplexity_score" in columns: |
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cond_filter = np.invert(np.all(conds["perplexity_score"], axis=0)) |
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display_dataset( |
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cond_filter, |
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"Discarded documents for the filter on the perplexity score", |
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) |
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display_dataset(all_conds, "Retained documents") |
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def filtering_of_words(self): |
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if not (self.words is None): |
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st.sidebar.subheader("Parameter of the filtering on words") |
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cutoff_def = "If the length of a word is higher than this number, the word is removed." |
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max_len_word = min(int(np.max(self.words["len_word"])) + 1, 200) |
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cutoff_word = st.sidebar.slider(cutoff_def, 0, max_len_word, max_len_word) |
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incorrect_substrings = st.sidebar.checkbox( |
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"Remove words with incorrect substrings." |
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) |
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cond_words = self.words["len_word"] <= cutoff_word |
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if incorrect_substrings: |
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cond_words = cond_words & np.invert(self.words["incorrect_substring"]) |
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st.header("Filtering on words") |
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st.markdown( |
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f"Since the number of words is way larger than the number of documents, " |
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f"we consider in this section words for the first {self.num_docs_for_words} documents only." |
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) |
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discarded_words = self.words.loc[np.invert(cond_words)] |
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st.subheader( |
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f"Discarded words: {len(discarded_words)} words ({len(discarded_words) / len(self.words) * 100:.2f}%)" |
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) |
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st.markdown( |
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"Click on a column to sort by it, place the cursor on the text to display it." |
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) |
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st.dataframe(discarded_words) |
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retained_words = self.words.loc[cond_words] |
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st.subheader( |
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f"Retained words: {len(retained_words)} words ({len(retained_words) / len(self.words) * 100:.2f}%)" |
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) |
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st.markdown( |
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"Click on a column to sort by it, place the cursor on the text to display it." |
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) |
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st.dataframe(retained_words) |
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def plot_distributions_filtering_parameters(self): |
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st.header("Distributions of the filtering parameters") |
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display_distributions = st.checkbox("Display distributions") |
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if display_distributions: |
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def plot_hist(dataframe, key, num_bins=50): |
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st.subheader(" ".join(key.split("_"))) |
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hist_values = dataframe[key].values |
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max_range = np.max(hist_values) |
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hist_values = np.histogram( |
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hist_values, bins=num_bins, range=(0, max_range) |
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)[0] |
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st.bar_chart(hist_values) |
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st.markdown(f"Each bin is of size: {max_range/num_bins}.") |
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for key in list({el[0]: None for el in self.keys}): |
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plot_hist(self.docs, key) |
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if not (self.words is None): |
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plot_hist(self.words, "len_word") |
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def plot_zipf_law(self): |
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if not (self.words is None): |
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st.header("Zipf's Law") |
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display_zipf_law = st.checkbox("Display Zipf's Law") |
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if display_zipf_law: |
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freq_words = {} |
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for _, row in self.words.iterrows(): |
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freq_words[row["word"]] = freq_words.get(row["word"], 0) + 1 |
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freq_words = np.array(list(freq_words.values())) |
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freq_words = -np.sort(-freq_words) |
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fig, ax = plt.subplots() |
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ax.loglog(freq_words) |
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ax.set_title("Zipf's Law") |
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ax.set_xlabel("$i$-th most frequent word") |
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ax.set_ylabel("frequency in the documents") |
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st.pyplot(fig) |
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def download_data(self): |
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st.header("Download data") |
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with open(self.path_data) as json_file: |
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btn = st.download_button( |
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label="Download data as json", |
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data=json_file, |
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file_name="data.json", |
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) |
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def visualization(self): |
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self.preamble() |
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self.open_data() |
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self.set_title() |
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self.filtering_of_docs() |
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self.filtering_of_words() |
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self.plot_distributions_filtering_parameters() |
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self.download_data() |
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path_instructions = "./explanation_filtering_pipeline.pdf" |
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path_data = "./en_examples_with_stats.json" |
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lang = "English" |
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num_docs = 5000 |
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num_docs_for_words = 500 |
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max_len_text_display = 10000 |
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visualization = Visualization( |
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path_instructions, |
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path_data, |
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lang, |
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num_docs, |
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num_docs_for_words, |
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max_len_text_display, |
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) |
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visualization.visualization() |
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