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| import json | |
| import math | |
| import os | |
| os.system("pip uninstall -y gradio") | |
| os.system("pip install gradio==3.26.0") | |
| 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", | |
| ] | |
| 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, vect__max_df, vect__min_df, vect__ngram_range, vect__norm): | |
| pipeline = Pipeline( | |
| [ | |
| ("vect", TfidfVectorizer()), | |
| ("clf", ComplementNB()), | |
| ] | |
| ) | |
| parameters_grid = { | |
| "vect__max_df": [eval(value) for value in vect__max_df.split(",")], | |
| "vect__min_df": [eval(value) for value in vect__min_df.split(",")], | |
| "vect__ngram_range": eval(vect__ngram_range), # unigrams or bigrams | |
| "vect__norm": [value.strip() for value in vect__norm.split(",")], | |
| "clf__alpha": np.logspace(-6, 6, 13), | |
| } | |
| print(parameters_grid) | |
| 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=parameters_grid, | |
| n_iter=40, | |
| random_state=0, | |
| n_jobs=2, | |
| verbose=1, | |
| ) | |
| random_search.fit(data_train.data, data_train.target) | |
| best_parameters = json.dumps( | |
| random_search.best_estimator_.get_params(), | |
| indent=4, | |
| sort_keys=True, | |
| default=str, | |
| ) | |
| 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 parameters_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 | |
| def load_description(name): | |
| with open(f"./descriptions/{name}.md", "r") as f: | |
| return f.read() | |
| 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(load_description("description_part1")) | |
| gr.Markdown(load_description("description_part2")) | |
| gr.Markdown(AUTHOR) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("""## CATEGORY SELECTION""") | |
| gr.Markdown(load_description("description_category_selection")) | |
| drop_categories = gr.Dropdown( | |
| CATEGORIES, | |
| value=["alt.atheism", "talk.religion.misc"], | |
| multiselect=True, | |
| label="Categories", | |
| info="Please select up to two categories that you want to receive training on.", | |
| max_choices=2, | |
| interactive=True, | |
| ) | |
| with gr.Row(): | |
| with gr.Tab("PARAMETERS GRID"): | |
| gr.Markdown(load_description("description_parameter_grid")) | |
| with gr.Row(): | |
| with gr.Column(): | |
| clf__alpha = gr.Textbox( | |
| label="Classifier Alpha (clf__alpha)", | |
| value="1.e-06, 1.e-05, 1.e-04", | |
| info="Due to practical considerations, this parameter was kept constant.", | |
| interactive=False, | |
| ) | |
| vect__max_df = gr.Textbox( | |
| label="Vectorizer max_df (vect__max_df)", | |
| value="0.2, 0.4, 0.6, 0.8, 1.0", | |
| info="Values ranging from 0 to 1.0, separated by a comma.", | |
| interactive=True, | |
| ) | |
| vect__min_df = gr.Textbox( | |
| label="Vectorizer min_df (vect__min_df)", | |
| value="1, 3, 5, 10", | |
| info="Values ranging from 0 to 1.0, separated by a comma, or integers separated by a comma. If float, the parameter represents a proportion of documents, integer absolute counts.", | |
| interactive=True, | |
| ) | |
| with gr.Column(): | |
| vect__ngram_range = gr.Textbox( | |
| label="Vectorizer ngram_range (vect__ngram_range)", | |
| value="(1, 1), (1, 2)", | |
| info="""Tuples of integer values separated by a comma. For example an `ngram_range` of `(1, 1)` means only unigrams, `(1, 2)` means unigrams and bigrams, and `(2, 2)` means only bigrams.""", | |
| interactive=True, | |
| ) | |
| vect__norm = gr.Textbox( | |
| label="Vectorizer norm (vect__norm)", | |
| value="l1, l2", | |
| info="'l1' or 'l2', separated by a comma", | |
| interactive=True, | |
| ) | |
| with gr.Tab("DESCRIPTION OF PARAMETERS"): | |
| gr.Markdown("""### Classifier Alpha""") | |
| gr.Markdown(load_description("parameter_grid/alpha")) | |
| gr.Markdown("""### Vectorizer max_df""") | |
| gr.Markdown(load_description("parameter_grid/max_df")) | |
| gr.Markdown("""### Vectorizer min_df""") | |
| gr.Markdown(load_description("parameter_grid/min_df")) | |
| gr.Markdown("""### Vectorizer ngram_range""") | |
| gr.Markdown(load_description("parameter_grid/ngram_range")) | |
| gr.Markdown("""### Vectorizer norm""") | |
| gr.Markdown(load_description("parameter_grid/norm")) | |
| with gr.Row(): | |
| gr.Markdown( | |
| """ | |
| ## 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, | |
| inputs=[ | |
| drop_categories, | |
| vect__max_df, | |
| vect__min_df, | |
| vect__ngram_range, | |
| vect__norm, | |
| ], | |
| outputs=[plot_trade, plot_coordinates, best_parameters, test_accuracy], | |
| ) | |
| app.launch() | |