May-16-2021, 12:04 AM
(This post was last modified: May-16-2021, 10:10 AM by Yoriz.
Edit Reason: Added code tags
)
I use following code to generate graph, but nothing display, can anyone help? Thank you!
# Visualize all the original dimensions import plotly.express as px df=px.data.iris() features=["sepal_width", "sepal_length", "petal_width", "petal_length"] fig=px.scatter_matrix(df, dimensions=features, color="species") fig.update_traces(diagonal_visible=False) fig.show() import plotly.express as px from sklearn.decomposition import PCA df = px.data.iris() features = ["sepal_width", "sepal_length", "petal_width", "petal_length"] import sklearn.decomposition as PCA pca = PCA() components = pca.fit_transform(df[features]) labels = { str(i): f"PC {i+1} ({var:.1f}%)" for i, var in enumerate(pca.explained_variance_ratio_ * 100) } fig = px.scatter_matrix( components, labels=labels, dimensions=range(4), color=df["species"] ) fig.update_traces(diagonal_visible=False) fig.show() # Visualize a subset of the principal components import pandas as pd import plotly.express as px from sklearn.decomposition import PCA from sklearn.datasets import load_boston boston = load_boston() df = pd.DataFrame(boston.data, columns=boston.feature_names) n_components = 4 pca = PCA(n_components=n_components) components = pca.fit_transform(df) total_var = pca.explained_variance_ratio_.sum() * 100 labels = {str(i): f"PC {i+1}" for i in range(n_components)} labels['color'] = 'Median Price' fig = px.scatter_matrix( components, color=boston.target, dimensions=range(n_components), labels=labels, title=f'Total Explained Variance: {total_var:.2f}%', ) fig.update_traces(diagonal_visible=False) fig.show()