import math import gradio as gr import numpy as np import pandas as pd import plotly.express as px from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import RandomizedSearchCV from sklearn.naive_bayes import ComplementNB from sklearn.pipeline import Pipeline CATEGORIES = [ "alt.atheism", "comp.graphics", "comp.os.ms-windows.misc", "comp.sys.ibm.pc.hardware", "comp.sys.mac.hardware", "comp.windows.x", "misc.forsale", "rec.autos", "rec.motorcycles", "rec.sport.baseball", "rec.sport.hockey", "sci.crypt", "sci.electronics", "sci.med", "sci.space", "soc.religion.christian", "talk.politics.guns", "talk.politics.mideast", "talk.politics.misc", "talk.religion.misc", ] PARAMETER_GRID = { "vect__max_df": (0.2, 0.4, 0.6, 0.8, 1.0), "vect__min_df": (1, 3, 5, 10), "vect__ngram_range": ((1, 1), (1, 2)), # unigrams or bigrams "vect__norm": ("l1", "l2"), "clf__alpha": np.logspace(-6, 6, 13), } def shorten_param(param_name): """Remove components' prefixes in param_name.""" if "__" in param_name: return param_name.rsplit("__", 1)[1] return param_name def train_model(categories): pipeline = Pipeline( [ ("vect", TfidfVectorizer()), ("clf", ComplementNB()), ] ) data_train = fetch_20newsgroups( subset="train", categories=categories, shuffle=True, random_state=42, remove=("headers", "footers", "quotes"), ) data_test = fetch_20newsgroups( subset="test", categories=categories, shuffle=True, random_state=42, remove=("headers", "footers", "quotes"), ) pipeline = Pipeline( [ ("vect", TfidfVectorizer()), ("clf", ComplementNB()), ] ) random_search = RandomizedSearchCV( estimator=pipeline, param_distributions=PARAMETER_GRID, n_iter=40, random_state=0, n_jobs=2, verbose=1, ) random_search.fit(data_train.data, data_train.target) best_parameters = random_search.best_estimator_.get_params() test_accuracy = random_search.score(data_test.data, data_test.target) cv_results = pd.DataFrame(random_search.cv_results_) cv_results = cv_results.rename(shorten_param, axis=1) param_names = [shorten_param(name) for name in PARAMETER_GRID.keys()] labels = { "mean_score_time": "CV Score time (s)", "mean_test_score": "CV score (accuracy)", } fig = px.scatter( cv_results, x="mean_score_time", y="mean_test_score", error_x="std_score_time", error_y="std_test_score", hover_data=param_names, labels=labels, ) fig.update_layout( title={ "text": "trade-off between scoring time and mean test score", "y": 0.95, "x": 0.5, "xanchor": "center", "yanchor": "top", } ) column_results = param_names + ["mean_test_score", "mean_score_time"] transform_funcs = dict.fromkeys(column_results, lambda x: x) # Using a logarithmic scale for alpha transform_funcs["alpha"] = math.log10 # L1 norms are mapped to index 1, and L2 norms to index 2 transform_funcs["norm"] = lambda x: 2 if x == "l2" else 1 # Unigrams are mapped to index 1 and bigrams to index 2 transform_funcs["ngram_range"] = lambda x: x[1] fig2 = px.parallel_coordinates( cv_results[column_results].apply(transform_funcs), color="mean_test_score", color_continuous_scale=px.colors.sequential.Viridis_r, labels=labels, ) fig2.update_layout( title={ "text": "Parallel coordinates plot of text classifier pipeline", "y": 0.99, "x": 0.5, "xanchor": "center", "yanchor": "top", } ) return fig, fig2, best_parameters, test_accuracy DESCRIPTION_PART1 = [ "The dataset used in this example is", "[The 20 newsgroups text dataset](https://scikit-learn.org/stable/datasets/real_world.html#newsgroups-dataset)", "which will be automatically downloaded, cached and reused for the document classification example.", ] DESCRIPTION_PART2 = [ "In this example, we tune the hyperparameters of", "a particular classifier using a", "[RandomizedSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV).", "For a demo on the performance of some other classifiers, see the", "[Classification of text documents using sparse features](https://scikit-learn.org/stable/auto_examples/text/plot_document_classification_20newsgroups.html#sphx-glr-auto-examples-text-plot-document-classification-20newsgroups-py) notebook.", ] AUTHOR = """ Created by [@dominguesm](https://huggingface.co/dominguesm) based on [scikit-learn docs](https://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_text_feature_extraction.html) """ with gr.Blocks(theme=gr.themes.Soft()) as app: with gr.Row(): with gr.Column(): gr.Markdown("# Sample pipeline for text feature extraction and evaluation") gr.Markdown(" ".join(DESCRIPTION_PART1)) gr.Markdown(" ".join(DESCRIPTION_PART2)) gr.Markdown(AUTHOR) with gr.Row(): with gr.Column(): gr.Markdown("""## CATEGORY SELECTION""") drop_categories = gr.Dropdown( CATEGORIES, value=["alt.atheism", "talk.religion.misc"], multiselect=True, label="Categories", info="Select the categories you want to train on.", max_choices=2, interactive=True, ) with gr.Row(): with gr.Column(): gr.Markdown( """ ## PARAMETERS GRID ```python { 'clf__alpha': array( [1.e-06, 1.e-05, 1.e-04,...] ), 'vect__max_df': (0.2, 0.4, 0.6, 0.8, 1.0), 'vect__min_df': (1, 3, 5, 10), 'vect__ngram_range': ((1, 1), (1, 2)), 'vect__norm': ('l1', 'l2') } ``` ## MODEL PIPELINE ```python pipeline = Pipeline( [ ("vect", TfidfVectorizer()), ("clf", ComplementNB()), ] ) ``` """ ) with gr.Row(): with gr.Column(): gr.Markdown("""## TRAINING""") with gr.Row(): brn_train = gr.Button("Train").style(container=False) gr.Markdown("## RESULTS") with gr.Row(): best_parameters = gr.Textbox(label="Best parameters") test_accuracy = gr.Textbox(label="Test accuracy") plot_trade = gr.Plot(label="") plot_coordinates = gr.Plot(label="") brn_train.click( train_model, [drop_categories], [plot_trade, plot_coordinates, best_parameters, test_accuracy], ) app.launch()