def generate_assets():
X_train, y_train, X_test, y_test = titanic_survive()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
explainer = ClassifierExplainer(
model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
dashboard = ExplainerDashboard(explainer,
[
ShapDependenceComposite(explainer, title="Test Tab!"),
ShapDependenceComposite,
"importances"
], title="Test Title!")
pkl_dir = Path.cwd() / "tests" / "cli_assets"
explainer.dump(pkl_dir / "explainer.joblib")
explainer.to_yaml(pkl_dir / "explainer.yaml")
dashboard.to_yaml(pkl_dir / "dashboard.yaml",
explainerfile=str(pkl_dir / "explainer.joblib"))
return None
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
self.model = RandomForestClassifier(n_estimators=5, max_depth=2)
self.model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
self.model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
self.dashboard = ExplainerDashboard(self.explainer,
[
ShapDependenceTab(self.explainer, title="Test Tab!"),
ShapDependenceTab,
"importances"
], title="Test Title!")
self.pkl_dir = Path.cwd() / "tests" / "cli_assets"
self.explainer.dump(self.pkl_dir / "explainer.joblib")
self.explainer.to_yaml(self.pkl_dir / "explainer.yaml")
self.dashboard.to_yaml(self.pkl_dir / "dashboard.yaml",
explainerfile=str(self.pkl_dir / "explainer.joblib"))
class DashboardTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
self.model = RandomForestClassifier(n_estimators=5, max_depth=2)
self.model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
self.model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
self.dashboard = ExplainerDashboard(self.explainer,
[
ShapDependenceTab(self.explainer, title="Test Tab!"),
ShapDependenceTab,
"importances"
], title="Test Title!")
self.pkl_dir = Path.cwd() / "tests" / "cli_assets"
self.explainer.dump(self.pkl_dir / "explainer.joblib")
self.explainer.to_yaml(self.pkl_dir / "explainer.yaml")
self.dashboard.to_yaml(self.pkl_dir / "dashboard.yaml",
explainerfile=str(self.pkl_dir / "explainer.joblib"))
def test_yaml(self):
yaml = self.dashboard.to_yaml()
self.assertIsInstance(yaml, str)
def test_yaml_dict(self):
yaml_dict = self.dashboard.to_yaml(return_dict=True)
self.assertIsInstance(yaml_dict, dict)
self.assertIn("dashboard", yaml_dict)
def test_load_config_joblib(self):
db = ExplainerDashboard.from_config(
self.pkl_dir / "explainer.joblib",
self.pkl_dir / "dashboard.yaml")
self.assertIsInstance(db, ExplainerDashboard)
def test_load_config_yaml(self):
db = ExplainerDashboard.from_config(
self.pkl_dir / "dashboard.yaml")
self.assertIsInstance(db, ExplainerDashboard)
def test_load_config_explainer(self):
db = ExplainerDashboard.from_config(
self.explainer, self.pkl_dir / "dashboard.yaml")
self.assertIsInstance(db, ExplainerDashboard)
def dashboard_exp(model, X_data, y_data):
import dash_bootstrap_components as dbc
from explainerdashboard import RegressionExplainer, ExplainerDashboard
ExplainerDashboard(
RegressionExplainer(model, X_data, y_data),
bootstrap=dbc.themes.SANDSTONE,
importances=True,
model_summary=False,
contributions=True,
whatif=True,
shap_dependence=False,
shap_interaction=False,
decision_trees=False,
hide_whatifindexselector=True,
hide_whatifprediction=True,
hide_inputeditor=False,
hide_whatifcontributiongraph=False,
hide_whatifcontributiontable=True,
hide_whatifpdp=False,
hide_predindexselector=True,
hide_predictionsummary=True,
hide_contributiongraph=False,
hide_pdp=False,
hide_contributiontable=True,
hide_dropna=True,
hide_range=True,
hide_depth=True,
hide_sort=True,
hide_sample=True, # hide sample size input on pdp component
hide_gridlines=True, # hide gridlines on pdp component
hide_gridpoints=True,
hide_cats_sort=
True, # hide the sorting option for categorical features
hide_cutoff=
True, # hide cutoff selector on classification components
hide_percentage=
True, # hide percentage toggle on classificaiton components
hide_log_x=
True, # hide x-axis logs toggle on regression plots
hide_log_y=
True, # hide y-axis logs toggle on regression plots
hide_ratio=True, # hide the residuals type dropdown
hide_points=
True, # hide the show violin scatter markers toggle
hide_winsor=True, # hide the winsorize input
hide_wizard=
True, # hide the wizard toggle in lift curve component
hide_star_explanation=True,
).run()
model = pickle.load(open(MODELS_DIR / 'general_model.pkl', 'rb'))
y = pd.read_csv(DATA_DIR / 'general_target.csv',
index_col=['Ticker']).drop(columns=['Date'])
X = pd.read_csv(DATA_DIR / f'general_features.csv',
index_col=['Ticker']).drop(columns=['Date'])
# Dashboard Explainer is fussy about Column Names
X.columns = X.columns.str.replace('.', '')
feature_names = model.get_booster().feature_names
feature_names = [x.replace('.', '') for x in feature_names]
model.get_booster().feature_names = feature_names
explainer = RegressionExplainer(model, X, y)
db = ExplainerDashboard(
explainer,
title="Stock Valuation Explainer",
description=
"Visit https://share.streamlit.io/gardnmi/fundamental-stock-prediction to see the model in use,",
shap_interaction=False,
precision='float32',
decision_trees=False)
db.to_yaml("dashboard.yaml",
explainerfile="explainer.joblib",
dump_explainer=True)
db = ExplainerDashboard.from_config("dashboard.yaml")
app = db.flask_server()
from unittest.mock import MagicMock
import sys
sys.modules["xgboost"] = MagicMock()
from explainerdashboard import RegressionExplainer, ExplainerDashboard
explainer = RegressionExplainer.from_file("explainer.joblib")
# you can override params during load from_config:
db = ExplainerDashboard.from_config(explainer, "dashboard.yaml", title="Test")
app = db.flask_server()
# run waitress-serve --port=8070 dashboard:app in command line
# Have to run on http://127.0.0.1:8050/ to work, to figure out if can work with streamlit or not.
# Import libraries here
from sklearn.ensemble import RandomForestClassifier
from explainerdashboard import ClassifierExplainer, ExplainerDashboard
from explainerdashboard.datasets import titanic_survive, feature_descriptions
X_train, y_train, X_test, y_test = titanic_survive()
model = RandomForestClassifier(n_estimators=50,
max_depth=10).fit(X_train, y_train)
explainer = ClassifierExplainer(model,
X_test,
y_test,
cats=['Sex', 'Deck', 'Embarked'],
descriptions=feature_descriptions,
labels=['Not survived', 'Survived'])
ExplainerDashboard(explainer).run()
from explainerdashboard import RegressionExplainer, ExplainerDashboard
from explainerdashboard.custom import *
from joblib import load
import pandas as pd
from sklearn.model_selection import train_test_split
# Load model & data
dec_tree = load('dec_tree_v3.joblib')
df_data = pd.read_csv('data_v3_enc.csv')
# Prepare & split data
X = df_data.drop(['Duration', 'Timestamp'], axis=1)
y = df_data.Duration
Xt, X_small, yt, y_small = train_test_split(X, y, test_size=0.01, random_state=0)
exp = RegressionExplainer(dec_tree, X_small, y_small, cats=['Day_of_week', 'Hour', 'Vehicle', 'Position'])
# Build
db = ExplainerDashboard(exp, [ShapDependenceComposite, WhatIfComposite], hide_whatifpdp=True)
# Save
exp.dump("explainer.joblib")
db.to_yaml("dashboard.yaml")
n_jobs=8,
num_parallel_tree=1,
objective='reg:squarederror',
random_state=42,
reg_alpha=1,
reg_lambda=0,
scale_pos_weight=1.0,
seed=42,
subsample=0.6000000000000001,
tree_method='exact',
validate_parameters=1,
verbosity=None)
X, Y = pd.read_csv('X.csv', index_col=0), pd.read_csv('Y.csv', index_col=0)
REmodel = model.fit(X, Y)
explainer = RegressionExplainer(REmodel,
X,
Y,
cats=cats,
descriptions=feature_descriptions,
units="$")
ExplainerDashboard(
explainer,
title='XGBoost Regression Model Explainer: Predicting House Prices',
description=
'This dashboard shows the inner workings of a fitted machine learning model, and explains its predictions.',
shap_interaction=False,
decision_trees=False).run(port=int(os.environ.get('PORT', 5000)))
def test_load_config_explainer(self):
db = ExplainerDashboard.from_config(
self.explainer, self.pkl_dir / "dashboard.yaml")
self.assertIsInstance(db, ExplainerDashboard)
print('Incoming request')
return Response('Hello')
model = CatBoostClassifier(cat_features=list(d.columns))
print(list(d.columns))
model.load_model("Diabetes (1).cbm")
y = pd.DataFrame()
y["class"] = d["class"]
d.pop("class")
y["class"], _ = y["class"].factorize()
y["class"] = 1 - y["class"]
db = ExplainerDashboard.from_config("dashboard.yaml")
def dashboard(request):
return db.app.index()
if __name__ == '__main__':
port = int(os.environ.get("PORT", 2000))
with Configurator() as config:
config.add_route('hello', '/')
config.add_view(dashboard, route_name='hello')
app = config.make_wsgi_app()
serve(app, host='0.0.0.0', port=port)
from explainerdashboard import ClassifierExplainer, RegressionExplainer, ExplainerDashboard
from explainerdashboard.datasets import *
pkl_dir = Path.cwd() / "pkls"
# classifier
X_train, y_train, X_test, y_test = titanic_survive()
model = RandomForestClassifier(n_estimators=50,
max_depth=5).fit(X_train, y_train)
clas_explainer = ClassifierExplainer(model,
X_test,
y_test,
cats=['Sex', 'Deck', 'Embarked'],
descriptions=feature_descriptions,
labels=['Not survived', 'Survived'])
_ = ExplainerDashboard(clas_explainer)
clas_explainer.dump(pkl_dir / "clas_explainer.joblib")
# regression
X_train, y_train, X_test, y_test = titanic_fare()
model = RandomForestRegressor(n_estimators=50,
max_depth=5).fit(X_train, y_train)
reg_explainer = RegressionExplainer(model,
X_test,
y_test,
cats=['Sex', 'Deck', 'Embarked'],
descriptions=feature_descriptions,
units="$")
_ = ExplainerDashboard(reg_explainer)
reg_explainer.dump(pkl_dir / "reg_explainer.joblib")
from explainerdashboard.explainers import RandomForestRegressionExplainer
from sklearn.ensemble import RandomForestRegressor
from sklearn import tree
from explainerdashboard import ClassifierExplainer, ExplainerDashboard, RegressionExplainer
from explainerdashboard.datasets import titanic_survive, feature_descriptions
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
imdb = pd.read_csv("Amélioration/Data/movies2.csv", encoding="latin-1")
imdb = imdb.rename(columns = {'11:14' :'film','7.2' : 'metascore','Crime' : 'genre', 'Greg Marcks':'realisateur', 'Henry Thomas' : 'acteur_1', 'Colin Hanks':'acteur_2' ,'6000000' : 'budget', '0': 'votes2', '0.1': 'vote', 'Aug 12, 2005': 'date'})
colonne = ['genre','acteur_1', 'acteur_2', 'realisateur']
imdb = pd.get_dummies(imdb, columns= colonne)
imdb = imdb.drop(columns= ['film', 'budget', 'votes2', 'vote', 'date'], axis = 1 )
X = imdb.loc[:, imdb.columns != 'metascore' ]
y = imdb.loc[:, imdb.columns == 'metascore' ]
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3, random_state=42)
model = RandomForestRegressor().fit(X_train, y_train)
explainer = RegressionExplainer(model, X_test, y_test)
db = ExplainerDashboard(explainer, title="Metascore de film",
whatif=False, # you can switch off tabs with bools
shap_interaction=False,
decision_trees=False)
ExplainerDashboard(explainer).run()
