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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()
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