def execute_report(
working_dir: str,
explainer: object,
project_info_file: str,
x_train: Optional[pd.DataFrame] = None,
y_train: Optional[pd.DataFrame] = None,
y_test: Optional[Union[pd.Series, pd.DataFrame]] = None,
config: Optional[dict] = None,
notebook_path: Optional[str] = None,
):
"""
Executes the base_report.ipynb notebook and saves the results in working_dir.
Parameters
----------
working_dir : str
Directory in which will be saved the executed notebook.
explainer : shapash.explainer.smart_explainer.SmartExplainer object
Compiled shapash explainer.
project_info_file : str
Path to the file used to display some information about the project in the report.
x_train : pd.DataFrame
DataFrame used for training the model.
y_train : pd.Series or pd.DataFrame
Series of labels in the training set.
y_test : pd.Series or pd.DataFrame
Series of labels in the test set.
config : dict, optional
Report configuration options.
notebook_path : str, optional
Path to the notebook used to generate the report. If None, the Shapash base report
notebook will be used.
"""
if config is None:
config = {}
explainer.save(path=os.path.join(working_dir, 'smart_explainer.pickle'))
if x_train is not None:
x_train.to_csv(os.path.join(working_dir, 'x_train.csv'))
if y_train is not None:
y_train.to_csv(os.path.join(working_dir, 'y_train.csv'))
if y_test is not None:
y_test.to_csv(os.path.join(working_dir, 'y_test.csv'))
root_path = get_project_root()
if notebook_path is None or notebook_path == '':
notebook_path = os.path.join(root_path, 'shapash', 'report',
'base_report.ipynb')
pm.execute_notebook(notebook_path,
os.path.join(working_dir, 'base_report.ipynb'),
parameters=dict(dir_path=working_dir,
project_info_file=project_info_file,
config=config))
def export_and_save_report(working_dir: str, output_file: str):
"""
Exports a previously executed notebook and saves it as a static HTML file.
Parameters
----------
working_dir : str
Path to the directory containing the executed notebook.
output_file : str
Path to the html file that will be created.
"""
exporter = HTMLExporter(exclude_input=True,
extra_template_basedirs=[os.path.join(get_project_root(), 'shapash', 'report', 'template')],
template_name='custom', exclude_anchor_links=True)
(body, resources) = exporter.from_filename(filename=os.path.join(working_dir, 'base_report.ipynb'))
with open(output_file, "w") as file:
file.write(body)
from shapash.utils.transform import inverse_transform, apply_postprocessing
from shapash.explainer.smart_explainer import SmartExplainer
from shapash.utils.io import load_yml
from shapash.utils.utils import get_project_root, truncate_str
from shapash.report.visualisation import print_md, print_html, print_css_style, convert_fig_to_html, \
print_javascript_misc
from shapash.report.data_analysis import perform_global_dataframe_analysis, perform_univariate_dataframe_analysis
from shapash.report.plots import generate_fig_univariate, generate_confusion_matrix_plot
from shapash.report.common import series_dtype, get_callable, compute_col_types, VarType, display_value
from shapash.webapp.utils.utils import round_to_k
logging.basicConfig(level=logging.INFO)
template_loader = jinja2.FileSystemLoader(
searchpath=os.path.join(get_project_root(), 'shapash', 'report', 'html'))
template_env = jinja2.Environment(loader=template_loader)
class ProjectReport:
"""
The ProjectReport class allows to generate general information about a
Data Science project.
It analyzes the data and the model used in order to provide interesting
insights that can be shared with non technical person.
Parameters
----------
explainer : shapash.explainer.smart_explainer.SmartExplainer
A shapash SmartExplainer object that has already be compiled.
project_info_file : str