mlinsights.plotting#
- mlinsights.plotting.gallery.plot_gallery_images(imgs, texts=None, width=4, return_figure=False, ax=None, folder_image=None, **figure)[source]#
Plots a gallery of images using matplotlib.
- Parameters:
imgs – list of images (filename, urls or Pillow objects),
texts – text to display (if None, print
'img % i'
)width – number of images on the same line (unused if imgs is a matrix)
figure – additional parameters when the figure is created
return_figure – return the figure as well as the axes
ax – None or existing axes, it should have the sam shape of imgs
folder_image – image paths may be relative to some folder, in that case, they should be relative to this folder
- Returns:
axes or (figure, axes) if return_figure is True
- mlinsights.plotting.visualize.pipeline2dot(pipe, data, **params)[source]#
Exports a scikit-learn pipeline to DOT language. See Visualize a scikit-learn pipeline for an example.
- Parameters:
pipe – scikit-learn pipeline
data – training data as a dataframe or a numpy array, or just a list with the variable names
params – additional params to draw the graph
- Returns:
string
Default options for the graph are:
options = { 'orientation': 'portrait', 'ranksep': '0.25', 'nodesep': '0.05', 'width': '0.5', 'height': '0.1', }
- mlinsights.plotting.visualize.pipeline2str(pipe, indent=3)[source]#
Exports a scikit-learn pipeline to text.
- Parameters:
pipe – scikit-learn pipeline
indent – indent
- Returns:
str
<<<
from sklearn.linear_model import LogisticRegression from sklearn.impute import SimpleImputer from sklearn.preprocessing import OneHotEncoder from sklearn.preprocessing import StandardScaler, MinMaxScaler from sklearn.compose import ColumnTransformer from sklearn.pipeline import Pipeline from mlinsights.plotting import pipeline2str numeric_features = ["age", "fare"] numeric_transformer = Pipeline( steps=[("imputer", SimpleImputer(strategy="median")), ("scaler", StandardScaler())] ) categorical_features = ["embarked", "sex", "pclass"] categorical_transformer = Pipeline( steps=[ ("imputer", SimpleImputer(strategy="constant", fill_value="missing")), ("onehot", OneHotEncoder(handle_unknown="ignore")), ] ) preprocessor = ColumnTransformer( transformers=[ ("num", numeric_transformer, numeric_features), ("cat", categorical_transformer, categorical_features), ] ) clf = Pipeline( steps=[ ("preprocessor", preprocessor), ("classifier", LogisticRegression(solver="lbfgs")), ] ) text = pipeline2str(clf) print(text)
>>>
Pipeline ColumnTransformer Pipeline(age,fare) SimpleImputer StandardScaler Pipeline(embarked,sex,pclass) SimpleImputer OneHotEncoder LogisticRegression