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zw>TG;ljrW|SNqEHzbgeRJfjb_4ecc3p)h^2LJ#7 literal 0 HcmV?d00001 diff --git a/chart_plotting.html b/chart_plotting.html new file mode 100644 index 0000000..f17db10 --- /dev/null +++ b/chart_plotting.html @@ -0,0 +1,2454 @@ + + + + + + + + + + +that-ml-library - chart_plotting + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

chart_plotting

+
+ +
+
+ This module contains several Python functions for Machine Learning models’ related plots +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
from that_ml_library.data_preprocess import *
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier
+from sklearn.preprocessing import LabelEncoder
+
+
+

Variance Inflation Factor and Correlation

+
+

source

+
+

get_vif

+
+
 get_vif (df:pandas.core.frame.DataFrame, plot_corr=False, figsize=(10,
+          10))
+
+

Perform variance inflation factor calculation, and optionally plot correlation matrix

+

Note that your dataframe should only have numerical features to perform VIF

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
dfpd.DataFramedataframe to plot
plot_corrboolFalseto plot the correlation matrix
figsizetuple(10, 10)Matplotlib figsize
+

1. Why you should use VIF: to detect multicollinearity (more than 2 columns)

+
    +
  • Compute variance inflation factor
    • +
  • The VIF is variance inflation factor the ratio of the variance of βˆj when fitting the full model (with other features) divided by the variance of βˆj if fit on its own +
      +
    • Min(VIF) = 1 (no collinearity)
      • +
    • VIF >5 or >10 means high collinearity
      • +
    • +
+

2. How to calculating VIF: Set the suspected collinearity feature (e.g. X1) as label, and try to predict X1 using a regression model and other features

+

3. What to do with high collinearity:

+
    +

  • Drop one of them

    • +

  • Combine them to create a new feature

    • +

  • Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression.

    • +
+
+
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/titanic.csv')
+df_num = process_missing_values(df[['Survived','Pclass','Age','SibSp','Parch']],
+                                missing_cols='Age',strategies='median')
+
+
+
get_vif(df_num,True,(5,5))
+
+
+
+

+
+
+
+
+
const       28.227667
+Survived     1.061895
+Pclass       1.173788
+Age          1.361584
+SibSp        1.351837
+Parch        1.199945
+dtype: float64
+
+
+
+

source

+
+
+

get_correlation_by_threshold

+
+
 get_correlation_by_threshold (df_corr, min_thres=0.98)
+
+ + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
df_corrCorrelation DataFrame
min_thresfloat0.98minimum correlation to take
+
+
get_correlation_by_threshold(df_num.corr(),min_thres=0)
+
+
{'Pclass': {'Survived': -0.11633986928104582},
+ 'Age': {'Survived': -0.11211373025858094, 'Pclass': -0.3451575619176082},
+ 'SibSp': {'Survived': -0.06694288369258686,
+  'Pclass': 0.08741953046914279,
+  'Age': -0.3664840343129444},
+ 'Parch': {'Survived': 0.03943462980865732,
+  'Pclass': 0.016490845192711254,
+  'Age': -0.19765444198507792,
+  'SibSp': 0.39904002232194297}}
+
+
+
+
get_correlation_by_threshold(df_num.corr(),min_thres=0.3)
+
+
{'Age': {'Pclass': -0.3451575619176082},
+ 'SibSp': {'Age': -0.3664840343129444},
+ 'Parch': {'SibSp': 0.39904002232194297}}
+
+
+
+

source

+
+
+

plot_cat_correlation

+
+
 plot_cat_correlation (df_cat, figsize=(10, 10))
+
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
df_catDataFrame with categorical features that have been processed
figsizetuple(10, 10)Matplotlib figsize
+

Let’s process some of the categorical features

+
+
from sklearn.preprocessing import OrdinalEncoder
+
+
+
for c in ['Sex','Embarked']:  
+    oe= OrdinalEncoder()
+    df[c] = oe.fit_transform(df[c].values.reshape(-1,1))
+
+
+
df_cat = df[['Survived','Pclass','Sex','Embarked']]
+
+
+
df_cat.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SurvivedPclassSexEmbarked
0031.02.0
1110.00.0
2130.02.0
3110.02.0
4031.02.0
+ +
+
+
+
+

Cramer’s V measures association between two nominal variables.

+

Cramer’s V lies between 0 and 1 (inclusive). - 0 indicates that the two variables are not linked by any relation. - 1 indicates that there exists a strong association between the two variables.

+
+
plot_cat_correlation(df_cat,(5,5))
+
+
+
+

+
+
+
+
+
+
cat_corr = get_cat_correlation(df_cat)
+get_correlation_by_threshold(cat_corr,min_thres=0.2)
+
+
{'Sex': {'Survived': 0.5650175790296367},
+ 'Embarked': {'Pclass': 0.23572003899034383}}
+
+
+
+
+
+

Evaluation plot for regression problem

+
+

source

+
+

plot_residuals

+
+
 plot_residuals (model, X_trn, y_trn, X_test=None, y_test=None,
+                 qqplot=True)
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
modelRegression model
X_trnTraining dataframe
y_trnTraining label
X_testNoneTypeNoneTesting dataframe
y_testNoneTypeNoneTesting label
qqplotboolTrueTo whether plot the qqplot
+
+
df_reg = pd.read_csv('http://www.statsci.org/data/general/uscrime.txt',sep='\t')
+
+
+
from sklearn.linear_model import LinearRegression
+
+
+
reg_model = LinearRegression()
+reg_model.fit(df_reg.drop('Crime',axis=1), df_reg.Crime.values)
+
+
LinearRegression()
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+
+
+
+
plot_residuals(reg_model, df_reg.drop('Crime',axis=1), df_reg.Crime.values, 
+               X_test=None, y_test=None, qqplot=True)
+
+
/home/quan/anaconda3/envs/dev/lib/python3.10/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
+
+
+
+
+

+
+
+
+
+
+
from sklearn.model_selection import train_test_split
+
+
+
X_train, X_test, y_train, y_test = train_test_split(df_reg.drop('Crime',axis=1), df_reg.Crime.values, 
+                                                    test_size=0.2, random_state=42)
+
+
+
plot_residuals(reg_model, X_train,y_train,X_test, y_test, qqplot=True)
+
+
/home/quan/anaconda3/envs/dev/lib/python3.10/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
+
+
+
+
+

+
+
+
+
+
+

source

+
+
+

plot_prediction_distribution

+
+
 plot_prediction_distribution (y_true, y_pred, figsize=(15, 5))
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
y_trueTrue label numpy array
y_predPrediction numpy array
figsizetuple(15, 5)Matplotlib figsize
+
+
reg_model = LinearRegression()
+reg_model.fit(df_reg.drop('Crime',axis=1), df_reg.Crime.values)
+
+
LinearRegression()
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+
+
+
+
y_pred = reg_model.predict(df_reg.drop('Crime',axis=1))
+y_true = df_reg.Crime.values
+
+
+
plot_prediction_distribution(y_true,y_pred)
+
+
MSE: 28828.633430503334
+RMSE: 169.789968580312
+MAE: 129.91521266409967
+
+
+
+
+

+
+
+
+
+
+
+
+

Model evaluation curves

+
+

source

+
+

plot_learning_curve

+
+
 plot_learning_curve (estimator, title, X, y, axes=None, ylim=None,
+                      cv=None, n_jobs=-1, scoring=None, train_sizes=[0.05,
+                      0.24, 0.43, 0.62, 0.81, 1.0], save_fig=False,
+                      figsize=(20, 5), seed=42)
+
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
estimatorsklearn’s classifier
titleTitle of the chart
XTraining features
yTraining label
axesNoneTypeNonematplotlib’s axes
ylimNoneTypeNoney axis range limit
cvNoneTypeNonesklearn’s cross-validation splitting strategy
n_jobsint-1Number of jobs to run in parallel
scoringNoneTypeNonemetric
train_sizeslist[0.05, 0.24, 0.43, 0.62, 0.81, 1.0]List of training size portion
save_figboolFalseTo store the chart as png in images directory
figsizetuple(20, 5)Matplotlib figsize
seedint42Random seed
+
+
dt = DecisionTreeClassifier(criterion='entropy',random_state=42,min_samples_leaf=1)
+plot_learning_curve(dt,'Learning Curve - Decision Tree - Titanic',df_num.drop('Survived',axis=1),df_num['Survived'],
+                      cv=5,scoring='f1_macro',train_sizes=np.linspace(0.1,1,20))
+
+
+
+

+
+
+
+
+
+

source

+
+
+

plot_validation_curve

+
+
 plot_validation_curve (estimator, title, X, y, ylim=None, cv=None,
+                        param_name=None, param_range=None, is_log=False,
+                        n_jobs=-1, scoring=None, save_fig=False,
+                        figsize=(8, 4), fill_between=True,
+                        enumerate_x=False)
+
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
estimatorsklearn’s classifier
titleTitle of the chart
XTraining features
yTraining label
ylimNoneTypeNoney axis range limit
cvNoneTypeNonesklearn’s cross-validation splitting strategy
param_nameNoneTypeNoneName of model’s hyperparameter
param_rangeNoneTypeNoneList containing range of value for param_name
is_logboolFalseTo log the value in param_range, for plotting
n_jobsint-1Number of jobs to run in parallel
scoringNoneTypeNonemetric
save_figboolFalseTo store the chart as png in images directory
figsizetuple(8, 4)Matplotlib figsize
fill_betweenboolTrueTo add a upper and lower one-std line for train and test curve
enumerate_xboolFalseConvert categorical hyperparam to numerical, for x axis
+
+
dt = DecisionTreeClassifier(criterion='entropy',random_state=42)
+plot_validation_curve(dt,'Val Curve - Decision Tree - Titanic',df_num.drop('Survived',axis=1),df_num['Survived'],
+                      cv=5,param_range=np.arange(1,20,1),param_name='max_depth',scoring='f1_macro')
+
+
+
+

+
+
+
+
+
+
+
+

Tree visualization

+
+

source

+
+

plot_tree_dtreeviz

+
+
 plot_tree_dtreeviz (estimator, X, y, target_name:str,
+                     class_names:list=None, tree_index=0,
+                     depth_range_to_display=None, fancy=False, scale=1.0)
+
+

Plot a decision tree using dtreeviz. Note that you need to install graphviz before using this function

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
estimatorsklearn’s classifier
XTraining features
yTraining label
target_namestrThe (string) name of the target variable; e.g., for Titanic, it’s “Survived”
class_nameslistNoneList of names associated with the labels (same order); e.g. [‘no’,‘yes’]
tree_indexint0Index (from 0) of tree if model is an ensemble of trees like a random forest.
depth_range_to_displayNoneTypeNoneRange of depth levels to be displayed. The range values are inclusive
fancyboolFalseTo draw fancy tree chart (as opposed to simplified one)
scalefloat1.0Scale of the chart. Higher means bigger
+
+
dt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None,max_depth=3)
+dt.fit(df_num.drop('Survived',axis=1),df_num['Survived'])
+
+
DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=42)
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+
+
+

After you have installed graphviz (https://github.com/parrt/dtreeviz#installation), run these codes to run dtreeviz

+
+
plot_tree_dtreeviz(dt,df_num.drop('Survived',axis=1),df_num['Survived'],
+                   target_name='Survived',
+                   class_names=['no','yes'],
+                   fancy=True,scale=1)
+
+
/home/quan/anaconda3/envs/dev/lib/python3.10/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names
+
+
+
+
+

+
+
+
+
+
+
plot_tree_dtreeviz(dt,df_num.drop('Survived',axis=1),df_num['Survived'],
+                   target_name='Survived',
+                   class_names=['no','yes'],
+                   depth_range_to_display=[2,3],
+                   fancy=True,scale=1.2)
+
+
/home/quan/anaconda3/envs/dev/lib/python3.10/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names
+
+
+
+
+

+
+
+
+
+
+

source

+
+
+

plot_classification_tree_sklearn

+
+
 plot_classification_tree_sklearn (estimator, feature_names,
+                                   class_names:list, rotate=True,
+                                   fname='tmp')
+
+

Plot a decision tree classifier using sklearn. Note that this will output a png file with fname instead of showing it in the notebook

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
estimatorsklearn’s classifier
feature_namesList of names of dependent variables (features)
class_nameslistList of names associated with the labels (same order); e.g. [‘no’,‘yes’]
rotateboolTrueTo rotate the tree graph
fnamestrtmpName of the png file to save(no extension)
+
+
# feature names (not including label)
+feature_names = df_num.drop('Survived',axis=1).columns.values
+print(feature_names)
+
+
['Pclass' 'Age' 'SibSp' 'Parch']
+
+
+

After you have installed graphviz (https://github.com/parrt/dtreeviz#installation), run these codes to run sklearn tree plotting

+
dt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None,max_depth=3)
+dt.fit(df_num.drop('Survived',axis=1),df_num['Survived'])
+plot_tree_sklearn(dt,feature_names=df_num.drop('Survived',axis=1).columns.values,
+                  class_names=['no','yes'],
+                  rotate=True,fname='tree_depth_3_titanic')
+

To show the image in notebook, create a markdown cell and type ![](images/tree_depth_3_titanic.png)

+

+
+
+
+

Decision Tree’s feature importances

+
+

source

+
+

plot_feature_importances

+
+
 plot_feature_importances (importances, feature_names, figsize=(20, 10),
+                           top_n=None)
+
+

Plot and return a dataframe of feature importances, using sklearn’s feature_importances_ value

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
importancesfeature importances from sklearn’s feature_importances_ variable
feature_namesList of names of dependent variables (features)
figsizetuple(20, 10)Matplotlib figsize
top_nNoneTypeNoneShow top n features
+
+
feature_names = df_num.drop('Survived',axis=1).columns.values
+
+
+
dt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None,max_depth=5)
+dt.fit(df_num.drop('Survived',axis=1),df_num['Survived'])
+
+plot_feature_importances(dt.feature_importances_,feature_names,top_n=3)
+
+
+
+

+
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
Importance
Feature
Pclass0.087868
SibSp0.186577
Age0.686647
+ +
+
+
+
+
+
feature_names = df_num.drop('Survived',axis=1).columns.values
+dt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None,max_depth=5)
+dt.fit(df_num.drop('Survived',axis=1),df_num['Survived'])
+
+plot_permutation_importances(dt,
+                             df_num.drop('Survived',axis=1),
+                             df_num['Survived'],
+                             scoring=['f1_macro','accuracy'],
+                             top_n=3)
+
+
f1_macro
+accuracy
+
+
+
+
+

+
+
+
+
+
+
+

+
+
+
+
+
[         Importance       STD
+ Feature                      
+ Pclass     0.049733  0.024178
+ SibSp      0.063374  0.018909
+ Age        0.126187  0.019904,
+          Importance       STD
+ Feature                      
+ Pclass     0.046154  0.014841
+ SibSp      0.070513  0.013446
+ Age        0.133333  0.021983]
+
+
+
+
feature_names = df_num.drop('Survived',axis=1).columns.values
+dt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None,max_depth=5)
+dt.fit(df_num.drop('Survived',axis=1),df_num['Survived'])
+
+plot_permutation_importances(dt,
+                             df_num.drop('Survived',axis=1),
+                             df_num['Survived'],
+                             scoring='f1_macro'
+                             )
+
+
f1_macro
+
+
+
+
+

+
+
+
+
+
[         Importance       STD
+ Feature                      
+ Parch      0.002853  0.005706
+ Pclass     0.049733  0.024178
+ SibSp      0.063374  0.018909
+ Age        0.126187  0.019904]
+
+
+
+
+
+

Hyperparameters visualization

+
+

source

+
+

params_2D_heatmap

+
+
 params_2D_heatmap (search_cv:dict, param1:str, param2:str,
+                    scoring:str='f1_macro', log_param1=False,
+                    log_param2=False, figsize=(20, 10), min_hm=None,
+                    max_hm=None, higher_is_better=True)
+
+

Plot 2D graph of metric value for each pair of hyperparameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
search_cvdictA dict with keys as column headers and values as columns. Typically an attribute (cv_results_) of GridSearchCV or RandomizedSearchCV
param1strName of the first hyperparameter
param2strName of the second hyperparameter
scoringstrf1_macroMetric name
log_param1boolFalseTo log the first hyperparameter
log_param2boolFalseTo log the second hyperparameter
figsizetuple(20, 10)Matplotlib figsize
min_hmNoneTypeNoneMinimum value for the metric to show
max_hmNoneTypeNoneMaximum value of the metric to show
higher_is_betterboolTrueSet if high metric is better
+
+
dt = RandomForestClassifier(random_state=42)
+param_grid={
+    'n_estimators': np.arange(2,20),
+    'min_samples_leaf': np.arange(1,80),
+}
+# Note: in order to use params_2D_heatmap, you should set scoring to a list, and set refit to False
+clf = RandomizedSearchCV(dt,param_grid,n_iter=100,
+                         scoring=['f1_macro'],n_jobs=-1,
+                         cv=5,verbose=1,random_state=42,refit=False)
+clf.fit(df_num.drop('Survived',axis=1),df_num['Survived'])
+
+
Fitting 5 folds for each of 100 candidates, totalling 500 fits
+
+
+
RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),
+                   n_iter=100, n_jobs=-1,
+                   param_distributions={'min_samples_leaf': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
+       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
+       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,
+       52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,
+       69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79]),
+                                        'n_estimators': array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+       19])},
+                   random_state=42, refit=False, scoring=['f1_macro'],
+                   verbose=1)
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+
+
+
+
params_2D_heatmap(clf.cv_results_,'n_estimators','min_samples_leaf',
+                  scoring='f1_macro',
+                  figsize=(20,10))
+
+
/tmp/ipykernel_4395/2399879344.py:14: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
+
+
+
+
+

+
+
+
+
+
+
params_2D_heatmap(clf.cv_results_,'n_estimators','min_samples_leaf',
+                  scoring='f1_macro',
+                  figsize=(20,10),min_hm=0.45)
+
+
/tmp/ipykernel_4395/2399879344.py:14: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
+
+
+
+
+

+
+
+
+
+
+
dt = RandomForestClassifier(random_state=42)
+param_grid={
+    'n_estimators': np.arange(2,20),
+    'min_samples_leaf': np.arange(1,80),
+    'max_features': [0.3,0.4,0.5,0.6,0.7,0.8,0.9,1],
+}
+# Note: in order to use params_2D_heatmap, you should set scoring to a list, and set refit to False
+clf = RandomizedSearchCV(dt,param_grid,n_iter=100,
+                         scoring=['f1_macro'],n_jobs=-1,
+                         cv=5,verbose=1,random_state=42,refit=False)
+clf.fit(df_num.drop('Survived',axis=1),df_num['Survived'])
+
+
Fitting 5 folds for each of 100 candidates, totalling 500 fits
+
+
+
RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),
+                   n_iter=100, n_jobs=-1,
+                   param_distributions={'max_features': [0.3, 0.4, 0.5, 0.6,
+                                                         0.7, 0.8, 0.9, 1],
+                                        'min_samples_leaf': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
+       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
+       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,
+       52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,
+       69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79]),
+                                        'n_estimators': array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+       19])},
+                   random_state=42, refit=False, scoring=['f1_macro'],
+                   verbose=1)
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+
+
+
+
params_3D_heatmap(clf.cv_results_,
+                  'n_estimators',
+                  'min_samples_leaf',
+                  'max_features',
+                  scoring='f1_macro')
+
+
+
+
+
+
+
+

Partial Dependency Plot

+
+

source

+
+

pdp_numerical_only

+
+
 pdp_numerical_only (model, X:pandas.core.frame.DataFrame,
+                     num_features:list, class_names:list, y_colors=None,
+                     ncols=2, nrows=2, figsize=(20, 16))
+
+

Plot PDP plot for numerical dependent variables

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
modelsklearn tree model that has been trained
Xpd.DataFramedataframe to perform pdp
num_featureslistA list of numerical features
class_nameslistList of names associated with the labels (same order); e.g. [‘no’,‘yes’]
y_colorsNoneTypeNoneList of colors associated with class_names
ncolsint2
nrowsint2
figsizetuple(20, 16)
+
+
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/titanic.csv')
+df = df[['Survived','Pclass','Sex','Age','SibSp','Parch','Embarked']].copy()
+df = preprocessing_general(df,
+                           missing_cols=['Age','Embarked'],
+                           missing_vals=np.NaN,
+                           strategies=['median','most_frequent'],
+                           cat_cols='Embarked',
+                           bi_cols='Sex'
+                          )
+
+
+
df.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SurvivedPclassAgeSibSpParchEmbarked_CEmbarked_QEmbarked_SSex_male
00322.010FalseFalseTrueTrue
11138.010TrueFalseFalseFalse
21326.000FalseFalseTrueFalse
31135.010FalseFalseTrueFalse
40335.000FalseFalseTrueTrue
+ +
+
+
+
+

To better showcase the interpretation of Partial Dependency Plot, we will reuse the Titanic dataset, but now the independent variable (the one we need to predict) will be Pclass (3 classes to predict)

+
+
params = {'n_estimators': 12, 'min_samples_leaf': 10, 'max_features': 0.8, 'class_weight': 'balanced'}
+X_trn = df.drop('Pclass',axis=1)
+y_trn = LabelEncoder().fit_transform(df['Pclass'])
+dt = RandomForestClassifier(**params)
+dt.fit(X_trn,y_trn)
+
+
RandomForestClassifier(class_weight='balanced', max_features=0.8,
+                       min_samples_leaf=10, n_estimators=12)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
+
+
+
+
pdp_numerical_only(dt,X_trn,num_features=['Age','SibSp'],class_names=['pclass_1','pclass_2','pclass_3'],nrows=2,ncols=1,figsize=(6,8))
+
+
+
+

+
+
+
+
+
+

source

+
+
+

pdp_categorical_only

+
+
 pdp_categorical_only (model, X:pandas.core.frame.DataFrame,
+                       cat_feature:list, class_names:list, y_colors=None,
+                       ymax=0.5, figsize=(20, 8))
+
+

Plot PDP plot for categorical dependent variables

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
modelsklearn tree model that has been trained
Xpd.DataFramedataframe to perform pdp
cat_featurelistA single categorical feature
class_nameslistList of names associated with the labels (same order); e.g. [‘no’,‘yes’]
y_colorsNoneTypeNoneList of colors associated with class_names
ymaxfloat0.5
figsizetuple(20, 8)
+
+
pdp_categorical_only(dt,X_trn,'Survived',
+                     class_names=['pclass_1','pclass_2','pclass_3'])
+
+
+
+

+
+
+
+
+
+
+

+
+
+
+
+
+
+

+
+
+
+
+
+
+

+
+
+
+
+
+

source

+
+
+

plot_ice_pair

+
+
 plot_ice_pair (model, X:pandas.core.frame.DataFrame, pair_features:list,
+                class_idx, figsize=(10, 4))
+
+

Plot ICE plot from a pair of numerical feature

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
modelsklearn tree model that has been trained
Xpd.DataFramedataframe to perform ice
pair_featureslista list of only 2 features
class_idxindex of the class to plot
figsizetuple(10, 4)
+
+
# For pclass_1
+plot_ice_pair(dt,X_trn,pair_features=['Age','SibSp'],class_idx=0,figsize=(8,3))
+
+
+
+

+
+
+
+
+
+
# For pclass_2
+plot_ice_pair(dt,X_trn,pair_features=['Age','SibSp'],class_idx=1,figsize=(8,3))
+
+
+
+

+
+
+
+
+
+
# For pclass_3
+plot_ice_pair(dt,X_trn,pair_features=['Age','SibSp'],class_idx=2,figsize=(8,3))
+
+
+
+

+
+
+
+
+
+
+
+

Other functions

+
+

source

+
+

plot_confusion_matrix

+
+
 plot_confusion_matrix (y_true:list|numpy.ndarray,
+                        y_pred:list|numpy.ndarray, labels=None)
+
+

Simple function to plot the confusion matrix

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
y_truelist | np.ndarrayA list/numpy array of true labels
y_predlist | np.ndarrayA list/numpy array of predictions
labelsNoneTypeNoneDisplay names matching the labels (same order).
+
+
y_true = np.array([1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0])
+y_pred = np.array([1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0])
+
+
+
plot_confusion_matrix(y_true,y_pred,labels=['Not Survived','Survived'])
+
+
+
+

+
+
+
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/data_preprocess.html b/data_preprocess.html new file mode 100644 index 0000000..b9b3569 --- /dev/null +++ b/data_preprocess.html @@ -0,0 +1,1720 @@ + + + + + + + + + + +that-ml-library - data_preprocess + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

data_preprocess

+
+ +
+
+ This module contains several Python function for simple data preprocessing for ML, such as handling missing values, minmax scaling, and one hot encoding +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

process_missing_values

+
+
 process_missing_values (X_train:pandas.core.frame.DataFrame,
+                         X_test:pandas.core.frame.DataFrame=None,
+                         missing_cols:Union[list,str]=[],
+                         missing_vals:Union[list,int,float,str]=nan,
+                         strategies:Union[list,str]='median', **kwargs)
+
+

Process columns with missing values using Sklearn SimpleInputer

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
X_trainpd.DataFrameTraining dataframe
X_testpd.DataFrameNoneTesting dataframe
missing_colslist | str[]A column name having missing values, or a list of such columns
missing_valslist | int | float | strnanA placeholder for missing values, or a list of placeholders for all columns in miss_cols
strategieslist | strmedianThe imputation strategy from sklearn, or a list of such values. Currently support ‘median’,‘mean’,‘most_frequent’
kwargs
+
+
df = pd.DataFrame([[7, 2, 3], [4, np.nan, 6], [10, 5, -1]],columns=['col1','col2','col3'])
+display(df)
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
col1col2col3
072.03
14NaN6
2105.0-1
+ +
+
+
+
+
+
df_processed = process_missing_values(df,missing_cols=['col2','col3'],missing_vals=[np.NaN,-1],strategy='mean')
+display(df_processed)
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
col1col2col3
072.03.0
143.56.0
2105.04.5
+ +
+
+
+
+
+
df_trn = pd.DataFrame([[7, 2, 3], [4, np.nan, 6], [10, 5, -1]],columns=['col1','col2','col3'])
+df_test = pd.DataFrame([[2, np.NaN, 3], [3, 1, -1]],columns=['col1','col2','col3'])
+display(df_trn,df_test)
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
col1col2col3
072.03
14NaN6
2105.0-1
+ +
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + +
col1col2col3
02NaN3
131.0-1
+ +
+
+
+
+
+
df_processed_trn,df_procesed_val= process_missing_values(df_trn,
+                                                         df_test,
+                                                         missing_cols=['col2','col3'],
+                                                         missing_vals=[np.NaN,-1],strategy='mean')
+display(df_processed_trn,df_procesed_val)
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
col1col2col3
072.03.0
143.56.0
2105.04.5
+ +
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + +
col1col2col3
023.53.0
131.04.5
+ +
+
+
+
+
+

source

+
+
+

scale_num_cols

+
+
 scale_num_cols (X_train:pandas.core.frame.DataFrame,
+                 X_test:pandas.core.frame.DataFrame=None,
+                 num_cols:Union[list,str]=[],
+                 scale_methods:Union[list,str]='minmax', **kwargs)
+
+

Scale numerical columns using Sklearn

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
X_trainpd.DataFrameTraining dataframe
X_testpd.DataFrameNoneTesting dataframe
num_colslist | str[]Name of the numerical column, or a list of such columns
scale_methodslist | strminmaxSklearn scaling method (‘minmax’ or ‘standard’), or a list of such methods
kwargs
+
+
df = pd.DataFrame([[7, 2, 3], [4, 2, 6], [10, 5, 1]],columns=['col1','col2','col3'])
+display(df)
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
col1col2col3
0723
1426
21051
+ +
+
+
+
+
+
df_processed = scale_num_cols(df,num_cols=['col1','col3'],scale_methods='standard')
+display(df_processed)
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
col1col2col3
00.0000002-0.162221
1-1.22474521.297771
21.2247455-1.135550
+ +
+
+
+
+
+

source

+
+
+

one_hot_cat

+
+
 one_hot_cat (X_train:pandas.core.frame.DataFrame,
+              X_test:pandas.core.frame.DataFrame=None,
+              cat_cols:Union[list,str]=[], bi_cols:Union[list,str]=[],
+              **kwargs)
+
+

Perform ‘get_dummies’ on categorical columns

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
X_trainpd.DataFrameTraining dataframe
X_testpd.DataFrameNoneTesting dataframe
cat_colslist | str[]Name of the categorical columns (non-binary), or a list of such columns
bi_colslist | str[]Name of the binary column, or a list of such columns
kwargs
+
+
df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],
+                   'C': [1, 2, 3]})
+display(df)
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ABC
0ab1
1ba2
2ac3
+ +
+
+
+
+
+
df_processed = one_hot_cat(df,cat_cols='B',bi_cols='A')
+display(df_processed)
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CB_aB_bB_cA_b
010.01.00.00.0
121.00.00.01.0
230.00.01.00.0
+ +
+
+
+
+
+

source

+
+
+

preprocessing_general

+
+
 preprocessing_general (X_train:pandas.core.frame.DataFrame,
+                        X_test:pandas.core.frame.DataFrame=None, **kwargs)
+
+

The main preprocessing functions, will perform:

+
    +

  • Fill missing values

    • +

  • Scale numerical columns

    • +

  • One-hot encode categorical columns

    • +
+

Remember to put in the appropriate keyword arguments for each of the preprocessings mentioned above

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
X_trainpd.DataFrameTraining dataframe
X_testpd.DataFrameNoneTesting dataframe
kwargs
+
+
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/titanic.csv')
+
+
+
# Select some useful features, for now
+df = df[['Survived','Pclass','Sex','Age','SibSp','Parch','Embarked']].copy()
+df.info()
+
+
<class 'pandas.core.frame.DataFrame'>
+RangeIndex: 156 entries, 0 to 155
+Data columns (total 7 columns):
+ #   Column    Non-Null Count  Dtype  
+---  ------    --------------  -----  
+ 0   Survived  156 non-null    int64  
+ 1   Pclass    156 non-null    int64  
+ 2   Sex       156 non-null    object 
+ 3   Age       126 non-null    float64
+ 4   SibSp     156 non-null    int64  
+ 5   Parch     156 non-null    int64  
+ 6   Embarked  155 non-null    object 
+dtypes: float64(1), int64(4), object(2)
+memory usage: 8.7+ KB
+
+
+
+
df.sample(5)
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SurvivedPclassSexAgeSibSpParchEmbarked
8412female17.000S
403male35.000S
10103maleNaN00S
4803maleNaN20C
11203male22.000S
+ +
+
+
+
+

Let’s perform a simple train/test split

+
+
from sklearn.model_selection import train_test_split
+
+
+
X_train, X_test, y_train, y_test = train_test_split(df.drop('Survived',axis=1), df['Survived'],
+                                                    test_size=0.2,
+                                                    random_state=42,
+                                                    stratify=df['Survived'])
+
+
+
X_train.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PclassSexAgeSibSpParchEmbarked
1423female24.010S
1342male25.000S
1202male21.020S
503male7.041S
1332female29.010S
+ +
+
+
+
+
+
X_test.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PclassSexAgeSibSpParchEmbarked
913male20.000S
1452male19.011S
1153male21.000S
1063female21.000S
92female14.010C
+ +
+
+
+
+
+
X_train_processed,X_test_processed = preprocessing_general(X_train,X_test,
+                                                           missing_cols=['Age','Embarked'],
+                                                           missing_vals=np.NaN,
+                                                           strategies=['median','most_frequent'],
+                                                           num_cols=['Age','SibSp','Parch'],
+                                                           scale_methods=['standard','minmax','minmax'],
+                                                           cat_cols='Embarked',
+                                                           bi_cols='Sex'
+                                                          )
+
+

Notice that I don’t add Pclass to the preprocessing function. That means this column will be left untouched

+
+
X_train_processed.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PclassAgeSibSpParchEmbarked_CEmbarked_QEmbarked_SSex_male
1423-0.3255260.20.00.00.01.00.0
1342-0.2527960.00.00.00.01.01.0
1202-0.5437160.40.00.00.01.01.0
503-1.5619380.80.20.00.01.01.0
13320.0381250.20.00.00.01.00.0
+ +
+
+
+
+
+
X_test_processed.head()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PclassAgeSibSpParchEmbarked_CEmbarked_QEmbarked_SSex_male
913-0.6164460.00.00.00.01.01.0
1452-0.6891760.20.20.00.01.01.0
1153-0.5437160.00.00.00.01.01.0
1063-0.5437160.00.00.00.01.00.0
92-1.0528270.20.01.00.00.00.0
+ +
+
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/end_to_end_example.html b/end_to_end_example.html new file mode 100644 index 0000000..9837f5e --- /dev/null +++ b/end_to_end_example.html @@ -0,0 +1,1658 @@ + + + + + + + + + + +that-ml-library - End-to-end tutorial - Classification + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

End-to-end tutorial - Classification

+
+ +
+
+ This should give you an idea on how this library works +
+
+ + +
+ + + + +
+ + + +
+ + + +

In this tutorial, we will use the Pima Indian dataset https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.

+
+
from that_ml_library.data_preprocess import *
+from that_ml_library.chart_plotting import *
+from that_ml_library.ml_helpers import *
+
+
+

Load data

+
+
import pandas as pd
+import numpy as np
+
+
+
df = pd.read_csv('https://raw.githubusercontent.com/anhquan0412/dataset/main/diabetes.csv')
+print(df.shape)
+display(df.sample(7))
+
+
(768, 9)
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
9278178404846.70.261420
547150664234234.70.718420
28849656174920.80.340260
90180550019.10.258210
9869350306428.70.356230
39701316640034.30.196221
5741143863033030.10.892230
+ +
+
+
+
+
+
df.info()
+
+
<class 'pandas.core.frame.DataFrame'>
+RangeIndex: 768 entries, 0 to 767
+Data columns (total 9 columns):
+ #   Column                    Non-Null Count  Dtype  
+---  ------                    --------------  -----  
+ 0   Pregnancies               768 non-null    int64  
+ 1   Glucose                   768 non-null    int64  
+ 2   BloodPressure             768 non-null    int64  
+ 3   SkinThickness             768 non-null    int64  
+ 4   Insulin                   768 non-null    int64  
+ 5   BMI                       768 non-null    float64
+ 6   DiabetesPedigreeFunction  768 non-null    float64
+ 7   Age                       768 non-null    int64  
+ 8   Outcome                   768 non-null    int64  
+dtypes: float64(2), int64(7)
+memory usage: 54.1 KB
+
+
+
+
+

Train test split and preprocessing

+
+
from sklearn.model_selection import train_test_split
+
+
+
X_train, X_test, y_train, y_test = train_test_split(df.iloc[:,:-1], df.Outcome,
+                                                    test_size=0.2,
+                                                    random_state=42,
+                                                    stratify=df.Outcome)
+
+
+
X_train.describe()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAge
count614.000000614.000000614.000000614.000000614.000000614.000000614.000000614.000000
mean3.819218120.90879569.44299720.77687378.66612431.9732900.47742833.366450
std3.31414831.56109318.40258115.856433107.7365727.8613640.33030011.833438
min0.0000000.0000000.0000000.0000000.0000000.0000000.08400021.000000
25%1.00000099.00000062.5000000.0000000.00000027.5000000.24500024.000000
50%3.000000117.00000072.00000023.00000040.50000032.3000000.38250029.000000
75%6.000000140.00000080.00000032.000000130.00000036.5000000.63925041.000000
max17.000000199.000000122.00000099.000000744.00000067.1000002.32900081.000000
+ +
+
+
+
+

For this dataset, we only need to perform scaling on numerical features, as there’s no categorical features to process. Note that scaling of numerical data is required for linear model such as linear regression, and not required for tree model, though it doesn’t hurt the performance of such tree model. For simplicity, we will scale the numerical features for all the models we are going to run below.

+
+
num_cols = df.columns[:-1].tolist()
+print(num_cols)
+
+
['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']
+
+
+

For Blood Pressure and Skin Thickness, a value of 0 does not make sense, so we will consider these as missing values. The strategy for filling in the missing values will be ‘median’

+
+
miss_cols = ['BloodPressure','SkinThickness']
+
+
+
X_train_processed,X_test_processed = preprocessing_general(X_train,X_test,
+                                                           miss_cols=miss_cols,
+                                                           missing_vals=0,strategies='median',
+                                                           num_cols=num_cols,
+                                                           scale_methods='minmax')
+
+
+
X_train_processed.describe()
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAge
count614.000000614.000000614.000000614.000000614.000000614.000000614.000000614.000000
mean0.2246600.6075820.5692050.2098670.1057340.4765020.1752460.206107
std0.1949500.1585980.1508410.1601660.1448070.1171590.1471270.197224
min0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%0.0588240.4974870.5122950.0000000.0000000.4098360.0717150.050000
50%0.1764710.5879400.5901640.2323230.0544350.4813710.1329620.133333
75%0.3529410.7035180.6557380.3232320.1747310.5439640.2473270.333333
max1.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
+ +
+
+
+
+
+
+

Machine Learning Model Experiments

+
+
from sklearn.model_selection import StratifiedKFold
+
+
+
cv = StratifiedKFold(5)
+
+
+

Sklearn Logistic Regression

+
+
df_train = X_train_processed.copy()
+df_train['has_diabetes'] = y_train
+
+
+
get_vif(df_train,plot_corr=True)
+
+
+
+

+
+
+
+
+
const                       40.937975
+Pregnancies                  1.441405
+Glucose                      1.573457
+BloodPressure                1.152426
+SkinThickness                1.529585
+Insulin                      1.538192
+BMI                          1.343058
+DiabetesPedigreeFunction     1.097619
+Age                          1.577323
+has_diabetes                 1.451467
+dtype: float64
+
+
+

With VIF, we can see that there’s not much colinearity in this dataset. Note that VIF >5 or >10 means high colinearity

+
+
run_logistic_regression(X_train_processed,y_train,
+                        multi_class='multinomial',
+                        solver='newton-cg',max_iter=10000)
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
FeaturesCoefficientsCoefficients Exp
0Intercept-4.2338880.014496
1Pregnancies0.9766792.655623
2Glucose3.67917039.613487
3BloodPressure-0.6795000.506870
4SkinThickness0.2171581.242540
5Insulin-0.4736940.622698
6BMI3.10669822.347132
7DiabetesPedigreeFunction0.8819842.415688
8Age0.4614671.586400
+ +
+
+
+
+
----------------------------------------------------------------------------------------------------
+Log loss:  0.46620685663024525
+----------------------------------------------------------------------------------------------------
+              precision    recall  f1-score   support
+
+           0       0.81      0.90      0.85       400
+           1       0.76      0.60      0.67       214
+
+    accuracy                           0.79       614
+   macro avg       0.78      0.75      0.76       614
+weighted avg       0.79      0.79      0.79       614
+
+
+
+
LogisticRegression(max_iter=10000, multi_class='multinomial', penalty=None,
+                   random_state=0, solver='newton-cg')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
+
+
+

Interpretation of coefficients in logistic regression: For a coefficient β1, given a one unit increase in one of the variables (say X1), the odds of X (p(X)/1-p(X)) will be multiplied by e^β1.

+

In this case, we can tell from coefficient of Glucose that: the odd of having diabetes will be multiplied by ~40 if Glucose level is increased by 1

+
+

Statsmodel

+
+
run_multinomial_statmodel(X_train_processed,y_train,add_constant=True)
+
+
Optimization terminated successfully.
+         Current function value: 0.466207
+         Iterations 6
+                          MNLogit Regression Results                          
+==============================================================================
+Dep. Variable:                Outcome   No. Observations:                  614
+Model:                        MNLogit   Df Residuals:                      605
+Method:                           MLE   Df Model:                            8
+Date:                Mon, 19 Feb 2024   Pseudo R-squ.:                  0.2789
+Time:                        11:43:30   Log-Likelihood:                -286.25
+converged:                       True   LL-Null:                       -396.97
+Covariance Type:            nonrobust   LLR p-value:                 1.909e-43
+============================================================================================
+               Outcome=1       coef    std err          z      P>|z|      [0.025      0.975]
+--------------------------------------------------------------------------------------------
+const                       -8.4678      0.805    -10.523      0.000     -10.045      -6.891
+Pregnancies                  1.9534      0.624      3.129      0.002       0.730       3.177
+Glucose                      7.3584      0.855      8.609      0.000       5.683       9.034
+BloodPressure               -1.3590      0.733     -1.855      0.064      -2.795       0.077
+SkinThickness                0.4343      0.788      0.551      0.581      -1.110       1.978
+Insulin                     -0.9474      0.856     -1.107      0.268      -2.625       0.730
+BMI                          6.2135      1.150      5.405      0.000       3.960       8.467
+DiabetesPedigreeFunction     1.7640      0.755      2.335      0.020       0.283       3.245
+Age                          0.9229      0.616      1.499      0.134      -0.284       2.130
+============================================================================================
+----------------------------------------------------------------------------------------------------
+Log loss:  0.46620685662395556
+----------------------------------------------------------------------------------------------------
+              precision    recall  f1-score   support
+
+           0       0.81      0.90      0.85       400
+           1       0.76      0.60      0.67       214
+
+    accuracy                           0.79       614
+   macro avg       0.78      0.75      0.76       614
+weighted avg       0.79      0.79      0.79       614
+
+
+
+
<statsmodels.discrete.discrete_model.MNLogit>
+
+
+
+
+
+

Decision Tree

+
+

Validation Curve

+
+
from sklearn.tree import DecisionTreeClassifier
+
+
+
dt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None)
+cv = StratifiedKFold(5)
+plot_validation_curve(dt,'Val Curve - Decision Tree - PID',X_train_processed,y_train,
+                      cv=cv,param_range=np.arange(1,20,1),param_name='max_depth',scoring='f1_macro',
+                      n_jobs=-1,figsize=(6,4))
+
+
+
+

+
+
+
+
+

This is the validation curve for hyperparameter max depth, which can be used to prune a decision tree.

+

For max depth, we can observe the overfitting behavior when max depth is too high, which means the tree is able to make unlimited splits to the extreme of classifying each of the training data points correctly, thus failing to generalize. On the other hand, a small depth is enough to achieve a good CV score (from depth 1 to 5). This is a telling sign that some features have strong signals to predict diabetes than others.

+
+
+

Learning curve

+
+
np.linspace(0.1,1,20)
+
+
array([0.1       , 0.14736842, 0.19473684, 0.24210526, 0.28947368,
+       0.33684211, 0.38421053, 0.43157895, 0.47894737, 0.52631579,
+       0.57368421, 0.62105263, 0.66842105, 0.71578947, 0.76315789,
+       0.81052632, 0.85789474, 0.90526316, 0.95263158, 1.        ])
+
+
+
+
dt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None,min_samples_leaf=27)
+plot_learning_curve(dt,'DT Learning Curve',X_train_processed,y_train,cv=cv,scoring='f1_macro',
+                    train_sizes=np.linspace(0.1,1,20))
+
+
+
+

+
+
+
+
+

Based on the learning curve for the best tuned decision tree, more data is better for the model to learn, as training score seems to convert, and CV score still slowly increases. However, as the gap between CV score and train score is closing, adding lots of data might not be beneficial. For the scalability of the model, the relationship between training time and fit time is linear, which makes sense as more data allows tree to grow more, which takes more time to fit and predict.

+
+
+

Tree plotting

+

Using Dtreeviz

+
+
dt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None,max_depth=3)
+dt.fit(X_train_processed,y_train)
+plot_tree_dtreeviz(dt,X_train_processed,y_train,target_name='has_diabetes',class_names=['no','yes'],
+                   fancy=True,scale=1.0)
+
+
/home/quan/anaconda3/envs/dev/lib/python3.10/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names
+
+
+
+
+

+
+
+
+
+

Glucose alone can make CV F1 score to reach 0.704, which is not far from the CV score of the best tuned model. With domain knowledge, we can confirm that high glucose is the number one indicator for diabetes, because diabetes patients are unable to effectively use insulin to break down glucose.

+

Using Sklearn tree plot

+
+
dt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None,max_depth=3)
+dt.fit(X_train_processed,y_train)
+plot_classification_tree_sklearn(dt,feature_names=X_train_processed.columns.values,
+                  class_names=['no','yes'],
+                  rotate=True,fname='tree_depth_3_PID')
+
+

Type ![](images/tree_depth_3_PID.png)

+

+
+
+
+

Model finetuning process

+
+
from sklearn.ensemble import RandomForestClassifier
+
+
+
param_grid = {
+    'n_estimators': np.arange(2,80),
+    'min_samples_leaf': np.arange(1,100),
+    'max_features': [0.3,0.4,0.5,0.6,0.7,0.8,0.9,1],
+}
+search_cv = tune_sklearn_model('RandomForest',param_grid,X_train_processed,y_train,
+                               is_regression=False,
+                               custom_cv=5,
+                               scoring='f1_macro',
+                               random_cv_iter=100,
+                               rank_show=7,
+                               show_split_scores=True)
+
+
Fitting 5 folds for each of 100 candidates, totalling 500 fits
+----------
+Rank 1
+Params: {'n_estimators': 4, 'min_samples_leaf': 23, 'max_features': 0.7}
+Train scores: [0.77, 0.76, 0.78, 0.76, 0.78]
+Mean train score: 0.768 +- 0.008
+Test scores:  [0.73, 0.77, 0.71, 0.7, 0.77]
+Mean test score: 0.738 +- 0.029
+----------
+Rank 2
+Params: {'n_estimators': 41, 'min_samples_leaf': 5, 'max_features': 0.5}
+Train scores: [0.88, 0.87, 0.86, 0.88, 0.87]
+Mean train score: 0.872 +- 0.009
+Test scores:  [0.71, 0.78, 0.71, 0.71, 0.77]
+Mean test score: 0.737 +- 0.031
+----------
+Rank 3
+Params: {'n_estimators': 31, 'min_samples_leaf': 10, 'max_features': 0.6}
+Train scores: [0.83, 0.81, 0.82, 0.82, 0.81]
+Mean train score: 0.819 +- 0.011
+Test scores:  [0.72, 0.77, 0.7, 0.69, 0.79]
+Mean test score: 0.737 +- 0.040
+----------
+Rank 4
+Params: {'n_estimators': 20, 'min_samples_leaf': 28, 'max_features': 0.5}
+Train scores: [0.77, 0.76, 0.78, 0.76, 0.78]
+Mean train score: 0.770 +- 0.008
+Test scores:  [0.75, 0.76, 0.71, 0.68, 0.78]
+Mean test score: 0.735 +- 0.037
+----------
+Rank 5
+Params: {'n_estimators': 18, 'min_samples_leaf': 94, 'max_features': 0.7}
+Train scores: [0.73, 0.73, 0.74, 0.75, 0.72]
+Mean train score: 0.735 +- 0.011
+Test scores:  [0.74, 0.76, 0.7, 0.71, 0.77]
+Mean test score: 0.735 +- 0.026
+----------
+Rank 6
+Params: {'n_estimators': 35, 'min_samples_leaf': 61, 'max_features': 0.8}
+Train scores: [0.74, 0.74, 0.77, 0.75, 0.73]
+Mean train score: 0.746 +- 0.011
+Test scores:  [0.8, 0.76, 0.69, 0.67, 0.76]
+Mean test score: 0.734 +- 0.045
+----------
+Rank 7
+Params: {'n_estimators': 35, 'min_samples_leaf': 8, 'max_features': 0.5}
+Train scores: [0.84, 0.82, 0.83, 0.84, 0.82]
+Mean train score: 0.832 +- 0.008
+Test scores:  [0.75, 0.76, 0.7, 0.71, 0.76]
+Mean test score: 0.734 +- 0.027
+----------
+Default Params
+Mean train score: 1.0 +- 0.0
+Mean test score: 0.731 +- 0.04
+
+
+
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 24 concurrent workers.
+[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    0.2s finished
+
+
+
+
params_3D_heatmap(search_cv,'n_estimators','min_samples_leaf','max_features','f1_macro')
+
+
+
+
+

By looking at the 3D map of these three hyperparameters, we might need to exclude max_features of 1 and min_samples_leaf that is more than 80. In fact, max_features of 0.8 and 0.9 shows the most promising set of hyperparameters, thus we can now simplied our search space to this:

+
+
param_grid = {
+    'n_estimators': np.arange(2,80),
+    'min_samples_leaf': np.arange(1,85),
+    'max_features': [0.8,0.9],
+}
+search_cv = tune_sklearn_model('RandomForest',param_grid,X_train_processed,y_train,custom_cv=5,
+                               scoring='f1_macro',
+                               random_cv_iter=200,
+                               rank_show=2,
+                               show_split_scores=True)
+
+
Fitting 5 folds for each of 200 candidates, totalling 1000 fits
+----------
+Rank 1
+Params: {'n_estimators': 12, 'min_samples_leaf': 12, 'max_features': 0.8}
+Train scores: [0.8, 0.8, 0.81, 0.82, 0.8]
+Mean train score: 0.806 +- 0.010
+Test scores:  [0.74, 0.79, 0.7, 0.7, 0.79]
+Mean test score: 0.743 +- 0.041
+----------
+Rank 2
+Params: {'n_estimators': 33, 'min_samples_leaf': 23, 'max_features': 0.8}
+Train scores: [0.78, 0.76, 0.77, 0.77, 0.76]
+Mean train score: 0.769 +- 0.007
+Test scores:  [0.75, 0.77, 0.71, 0.69, 0.79]
+Mean test score: 0.742 +- 0.038
+----------
+Default Params
+Mean train score: 1.0 +- 0.0
+Mean test score: 0.731 +- 0.04
+
+
+
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 24 concurrent workers.
+[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    0.2s finished
+
+
+
+
params_2D_heatmap(search_cv,'n_estimators','min_samples_leaf','f1_macro',
+                  figsize=(20,10),min_hm=0.7,max_hm=None)
+
+
+
+

+
+
+
+
+

We will now focus on the area near the bottom right of the graph, for grid search

+
+
param_grid = {
+    'n_estimators': np.arange(62,73),
+    'min_samples_leaf': np.arange(20,26),
+    'max_features': [0.9],
+}
+search_cv = tune_sklearn_model('RandomForest',
+                              param_grid,
+                              X_train_processed,
+                              y_train,
+                              scoring='f1_macro',
+                              custom_cv=5,
+                              rank_show=5)
+
+
Fitting 5 folds for each of 66 candidates, totalling 330 fits
+----------
+Rank 1
+Params: {'max_features': 0.9, 'min_samples_leaf': 25, 'n_estimators': 72}
+Train scores: [0.77, 0.75, 0.78, 0.76, 0.77]
+Mean train score: 0.767 +- 0.009
+Test scores:  [0.74, 0.78, 0.7, 0.72, 0.78]
+Mean test score: 0.745 +- 0.033
+----------
+Rank 1
+Params: {'max_features': 0.9, 'min_samples_leaf': 25, 'n_estimators': 63}
+Train scores: [0.78, 0.75, 0.77, 0.77, 0.77]
+Mean train score: 0.765 +- 0.009
+Test scores:  [0.74, 0.78, 0.7, 0.72, 0.78]
+Mean test score: 0.745 +- 0.033
+----------
+Rank 1
+Params: {'max_features': 0.9, 'min_samples_leaf': 25, 'n_estimators': 65}
+Train scores: [0.78, 0.75, 0.78, 0.76, 0.77]
+Mean train score: 0.766 +- 0.009
+Test scores:  [0.74, 0.78, 0.7, 0.72, 0.78]
+Mean test score: 0.745 +- 0.033
+----------
+Rank 1
+Params: {'max_features': 0.9, 'min_samples_leaf': 25, 'n_estimators': 62}
+Train scores: [0.77, 0.75, 0.77, 0.77, 0.77]
+Mean train score: 0.765 +- 0.008
+Test scores:  [0.74, 0.78, 0.7, 0.72, 0.78]
+Mean test score: 0.745 +- 0.033
+----------
+Rank 5
+Params: {'max_features': 0.9, 'min_samples_leaf': 25, 'n_estimators': 67}
+Train scores: [0.78, 0.75, 0.77, 0.76, 0.77]
+Mean train score: 0.766 +- 0.011
+Test scores:  [0.74, 0.78, 0.7, 0.71, 0.78]
+Mean test score: 0.743 +- 0.034
+----------
+Default Params
+Mean train score: 1.0 +- 0.0
+Mean test score: 0.731 +- 0.04
+
+
+
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 24 concurrent workers.
+[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    0.2s finished
+
+
+
+
+

Feature importance (permutation technique)

+
+
params= {'max_features': 0.9, 'min_samples_leaf': 25, 'n_estimators': 62}
+# Mean train score: 0.765 +- 0.008
+# Mean test score: 0.745 +- 0.033
+
+
+
# # turn off warnings
+# def warn(*args, **kwargs):
+#     pass
+# import warnings
+# warnings.warn = warn
+
+
+
final_model= run_sklearn_model('RandomForest',
+                                         params,
+                                         X_train_processed,y_train,
+                                         is_regression=False,
+                                         class_names=['no','yes'],
+                                         test_split=None,
+                                         seed=42,
+                                         plot_fea_imp=True)
+
+
------------------------------ Train set ------------------------------
+Log loss: 0.4134397147185217
+              precision    recall  f1-score   support
+
+          no       0.82      0.90      0.86       400
+         yes       0.78      0.63      0.70       214
+
+    accuracy                           0.81       614
+   macro avg       0.80      0.77      0.78       614
+weighted avg       0.80      0.81      0.80       614
+
+
+
+
+
+

+
+
+
+
+

Insulin is not an impact feature for this dataset. Digging deeper on the source of the data, there’s a description that said: “This population has minimal European admixture, and their diabetes appears to be exclusively type 2 diabetes, with no evidence of the autoimmunity characteristic of type 1 diabetes”. Type 2 diabetes patients can still produce insulin, but the body is unable to use it. This information about insulin can be useful later if we want to perform feature selection, in order to boost this model’s performance

+
+
+

Partial Dependency Plot

+
+
num_features = X_train_processed.columns.tolist()
+num_features
+
+
['Pregnancies',
+ 'Glucose',
+ 'BloodPressure',
+ 'SkinThickness',
+ 'Insulin',
+ 'BMI',
+ 'DiabetesPedigreeFunction',
+ 'Age']
+
+
+
+
pdp_numerical_only(final_model,X_train_processed,num_features,class_names=['no diabetes','diabetes'],
+                  nrows=4,ncols=2)
+# y_class is ignored in binary classification or classical regression settings.
+
+
+
+

+
+
+
+
+

For Partial Dependency Plot, Glucose, BMI and Age show the most changes compared to other features. This matches the feature importances plot earlier.

+
+
plot_ice_pair(final_model,X_train_processed,pair_features=['Glucose','Age'],class_idx=1,figsize=(10,4))
+# class_idx is ignored in binary classification or classical regression settings.
+
+
+
+

+
+
+
+
+ + +
+
+ +
+ +
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diff --git a/index.html b/index.html new file mode 100644 index 0000000..7459a6b --- /dev/null +++ b/index.html @@ -0,0 +1,637 @@ + + + + + + + + + + +that-ml-library + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

that-ml-library

+
+ +
+
+ A useful package for EDA and quick ML model building +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

Install

+
pip install that_ml_library
+

For tree visualization function (plot_tree_dtreeviz or plot_tree_sklearn), you also need to install graphviz. Please follow the instruction here

+
+
+

How to use

+

Please visit https://anhquan0412.github.io/that-ml-library/ for tutorials and documentations

+
+
+

A word of caution

+

This library should only be utilized solely for developing a proof of concept or prototype for your machine learning model with your specific dataset, with the aim of evaluating the model’s performance and interpretability. For deployment in a production environment, opt for a more organized methodology, such as https://scikit-learn.org/stable/modules/compose.html#pipeline

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/ml_helpers.html b/ml_helpers.html new file mode 100644 index 0000000..4ed978d --- /dev/null +++ b/ml_helpers.html @@ -0,0 +1,943 @@ + + + + + + + + + + +that-ml-library - ml_helpers + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

ml_helpers

+
+ +
+
+ This module contains several Python functions for running a quick ML prototype on your processed dataset. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

run_logistic_regression

+
+
 run_logistic_regression (X_trn:pandas.core.frame.DataFrame,
+                          y_trn:Union[pandas.core.series.Series,numpy.ndar
+                          ray], multi_class='multinomial', solver='newton-
+                          cg', penalty=None, max_iter=10000,
+                          return_coef=False)
+
+

Perform Sklearn logistic regression, then print coefficients and classification report

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
X_trnpd.DataFrameTraining dataframe
y_trnpd.Series | np.ndarrayTraining label
multi_classstrmultinomialsklearn’s log reg multiclass option
solverstrnewton-cgsklearn’s log reg solver option
penaltyNoneTypeNonesklearn’s log reg penalty option
max_iterint10000sklearn’s log reg max iteration option
return_coefboolFalsewhether to return coefficients
+
+

source

+
+
+

run_multinomial_statmodel

+
+
 run_multinomial_statmodel (X_trn:pandas.core.frame.DataFrame,
+                            y_trn:Union[pandas.core.series.Series,numpy.nd
+                            array], add_constant=True)
+
+

Perform multinominal logit from statsmodel, then print results and classification report

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
X_trnpd.DataFrameTraining dataframe
y_trnpd.Series | np.ndarrayTraining label
add_constantboolTrueTo add a constant column to X_trn
+
+

source

+
+
+

run_sklearn_model

+
+
 run_sklearn_model (model_name:str, model_params:dict,
+                    X_trn:pandas.core.frame.DataFrame,
+                    y_trn:Union[pandas.core.series.Series,numpy.ndarray],
+                    is_regression=False, class_names:list=None,
+                    test_split=None, metric_funcs={}, seed=42,
+                    plot_fea_imp=True)
+
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
model_namestrsklearn’s Machine Learning model to try. Currently support DecisionTree,AdaBoost,RandomForest
model_paramsdictA dictionary containing model’s hyperparameters
X_trnpd.DataFrameTraining dataframe
y_trnpd.Series | np.ndarrayTraining label
is_regressionboolFalseTo use regression model or classification model
class_nameslistNoneList of names associated with the labels (same order); e.g. [‘no’,‘yes’]. For classification only
test_splitNoneTypeNoneTest set split. If float: random split. If list of list: indices of train and test set. If None: skip splitting
metric_funcsdict{}Dictionary of metric functions: {metric_name:metric_func}
seedint42Random seed
plot_fea_impboolTrueTo whether plot sklearn’s feature importances. Set to False to skip the plot
+
+

source

+
+
+

tune_sklearn_model

+
+
 tune_sklearn_model (model_name:str, param_grid:dict,
+                     X_trn:pandas.core.frame.DataFrame,
+                     y_trn:Union[pandas.core.series.Series,numpy.ndarray],
+                     is_regression=False, custom_cv=5,
+                     random_cv_iter=None, scoring=None, seed=42,
+                     rank_show=10, show_split_scores=True)
+
+

Perform either Sklearn’s Grid Search or Randomized Search (based on random_cv_iter) of the model using param_grid

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
model_namestrsklearn’s Machine Learning model to try. Currently support DecisionTree,AdaBoost,RandomForest,
param_griddictDictionary with parameters names (str) as keys and lists of parameter settings to try as values
X_trnpd.DataFrameTraining dataframe
y_trnpd.Series | np.ndarrayTraining label
is_regressionboolFalseIs it a regression problem, or classification?
custom_cvint5sklearn’s cross-validation splitting strategy
random_cv_iterNoneTypeNoneNumber of parameter settings that are sampled. Use this if you want to do RandomizedSearchCV
scoringNoneTypeNoneMetric
seedint42Random seed
rank_showint10Number of ranks to show (descending order)
show_split_scoresboolTrueTo show both train and test split scores
+
+

source

+
+
+

get_adaboost_info

+
+
 get_adaboost_info (dt_params, ada_params, X, y, seed=42)
+
+
+

source

+
+
+

show_both_cv

+
+
 show_both_cv (search_cv, default_cv, scoring, top_n=10,
+               show_split_scores=False)
+
+
+

source

+
+
+

summarize_default_cv

+
+
 summarize_default_cv (default_cv, s)
+
+
+

source

+
+
+

summarize_cv_results

+
+
 summarize_cv_results (search_cv, scoring, top_n=10,
+                       show_split_scores=False)
+
+
+

source

+
+ + +
+ +
+ + + + + \ No newline at end of file diff --git a/robots.txt b/robots.txt new file mode 100644 index 0000000..e96849b --- /dev/null +++ b/robots.txt @@ -0,0 +1 @@ +Sitemap: https://anhquan0412.github.io/that-ml-library/sitemap.xml diff --git a/search.json b/search.json new file mode 100644 index 0000000..0a539b6 --- /dev/null +++ b/search.json @@ -0,0 +1,232 @@ +[ + { + "objectID": "chart_plotting.html", + "href": "chart_plotting.html", + "title": "chart_plotting", + "section": "", + "text": "from that_ml_library.data_preprocess import *\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier\nfrom sklearn.preprocessing import LabelEncoder", + "crumbs": [ + "chart_plotting" + ] + }, + { + "objectID": "chart_plotting.html#variance-inflation-factor-and-correlation", + "href": "chart_plotting.html#variance-inflation-factor-and-correlation", + "title": "chart_plotting", + "section": "Variance Inflation Factor and Correlation", + "text": "Variance Inflation Factor and Correlation\n\nsource\n\nget_vif\n\n get_vif (df:pandas.core.frame.DataFrame, plot_corr=False, figsize=(10,\n 10))\n\nPerform variance inflation factor calculation, and optionally plot correlation matrix\nNote that your dataframe should only have numerical features to perform VIF\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\ndf\npd.DataFrame\n\ndataframe to plot\n\n\nplot_corr\nbool\nFalse\nto plot the correlation matrix\n\n\nfigsize\ntuple\n(10, 10)\nMatplotlib figsize\n\n\n\n1. Why you should use VIF: to detect multicollinearity (more than 2 columns)\n\nCompute variance inflation factor\nThe VIF is variance inflation factor the ratio of the variance of βˆj when fitting the full model (with other features) divided by the variance of βˆj if fit on its own\n\nMin(VIF) = 1 (no collinearity)\nVIF >5 or >10 means high collinearity\n\n\n2. How to calculating VIF: Set the suspected collinearity feature (e.g. X1) as label, and try to predict X1 using a regression model and other features\n3. What to do with high collinearity:\n\nDrop one of them\nCombine them to create a new feature\nPerform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression.\n\n\ndf = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/titanic.csv')\ndf_num = process_missing_values(df[['Survived','Pclass','Age','SibSp','Parch']],\n missing_cols='Age',strategies='median')\n\n\nget_vif(df_num,True,(5,5))\n\n\n\n\n\n\n\n\nconst 28.227667\nSurvived 1.061895\nPclass 1.173788\nAge 1.361584\nSibSp 1.351837\nParch 1.199945\ndtype: float64\n\n\n\nsource\n\n\nget_correlation_by_threshold\n\n get_correlation_by_threshold (df_corr, min_thres=0.98)\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\ndf_corr\n\n\nCorrelation DataFrame\n\n\nmin_thres\nfloat\n0.98\nminimum correlation to take\n\n\n\n\nget_correlation_by_threshold(df_num.corr(),min_thres=0)\n\n{'Pclass': {'Survived': -0.11633986928104582},\n 'Age': {'Survived': -0.11211373025858094, 'Pclass': -0.3451575619176082},\n 'SibSp': {'Survived': -0.06694288369258686,\n 'Pclass': 0.08741953046914279,\n 'Age': -0.3664840343129444},\n 'Parch': {'Survived': 0.03943462980865732,\n 'Pclass': 0.016490845192711254,\n 'Age': -0.19765444198507792,\n 'SibSp': 0.39904002232194297}}\n\n\n\nget_correlation_by_threshold(df_num.corr(),min_thres=0.3)\n\n{'Age': {'Pclass': -0.3451575619176082},\n 'SibSp': {'Age': -0.3664840343129444},\n 'Parch': {'SibSp': 0.39904002232194297}}\n\n\n\nsource\n\n\nplot_cat_correlation\n\n plot_cat_correlation (df_cat, figsize=(10, 10))\n\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\ndf_cat\n\n\nDataFrame with categorical features that have been processed\n\n\nfigsize\ntuple\n(10, 10)\nMatplotlib figsize\n\n\n\nLet’s process some of the categorical features\n\nfrom sklearn.preprocessing import OrdinalEncoder\n\n\nfor c in ['Sex','Embarked']: \n oe= OrdinalEncoder()\n df[c] = oe.fit_transform(df[c].values.reshape(-1,1))\n\n\ndf_cat = df[['Survived','Pclass','Sex','Embarked']]\n\n\ndf_cat.head()\n\n\n\n\n\n\n\n\n\nSurvived\nPclass\nSex\nEmbarked\n\n\n\n\n0\n0\n3\n1.0\n2.0\n\n\n1\n1\n1\n0.0\n0.0\n\n\n2\n1\n3\n0.0\n2.0\n\n\n3\n1\n1\n0.0\n2.0\n\n\n4\n0\n3\n1.0\n2.0\n\n\n\n\n\n\n\n\nCramer’s V measures association between two nominal variables.\nCramer’s V lies between 0 and 1 (inclusive). - 0 indicates that the two variables are not linked by any relation. - 1 indicates that there exists a strong association between the two variables.\n\nplot_cat_correlation(df_cat,(5,5))\n\n\n\n\n\n\n\n\n\ncat_corr = get_cat_correlation(df_cat)\nget_correlation_by_threshold(cat_corr,min_thres=0.2)\n\n{'Sex': {'Survived': 0.5650175790296367},\n 'Embarked': {'Pclass': 0.23572003899034383}}", + "crumbs": [ + "chart_plotting" + ] + }, + { + "objectID": "chart_plotting.html#evaluation-plot-for-regression-problem", + "href": "chart_plotting.html#evaluation-plot-for-regression-problem", + "title": "chart_plotting", + "section": "Evaluation plot for regression problem", + "text": "Evaluation plot for regression problem\n\nsource\n\nplot_residuals\n\n plot_residuals (model, X_trn, y_trn, X_test=None, y_test=None,\n qqplot=True)\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nmodel\n\n\nRegression model\n\n\nX_trn\n\n\nTraining dataframe\n\n\ny_trn\n\n\nTraining label\n\n\nX_test\nNoneType\nNone\nTesting dataframe\n\n\ny_test\nNoneType\nNone\nTesting label\n\n\nqqplot\nbool\nTrue\nTo whether plot the qqplot\n\n\n\n\ndf_reg = pd.read_csv('http://www.statsci.org/data/general/uscrime.txt',sep='\\t')\n\n\nfrom sklearn.linear_model import LinearRegression\n\n\nreg_model = LinearRegression()\nreg_model.fit(df_reg.drop('Crime',axis=1), df_reg.Crime.values)\n\nLinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.LinearRegressionLinearRegression()\n\n\n\nplot_residuals(reg_model, df_reg.drop('Crime',axis=1), df_reg.Crime.values, \n X_test=None, y_test=None, qqplot=True)\n\n/home/quan/anaconda3/envs/dev/lib/python3.10/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n\n\n\n\n\n\n\n\n\n\nfrom sklearn.model_selection import train_test_split\n\n\nX_train, X_test, y_train, y_test = train_test_split(df_reg.drop('Crime',axis=1), df_reg.Crime.values, \n test_size=0.2, random_state=42)\n\n\nplot_residuals(reg_model, X_train,y_train,X_test, y_test, qqplot=True)\n\n/home/quan/anaconda3/envs/dev/lib/python3.10/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n\n\n\n\n\n\n\n\n\n\nsource\n\n\nplot_prediction_distribution\n\n plot_prediction_distribution (y_true, y_pred, figsize=(15, 5))\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\ny_true\n\n\nTrue label numpy array\n\n\ny_pred\n\n\nPrediction numpy array\n\n\nfigsize\ntuple\n(15, 5)\nMatplotlib figsize\n\n\n\n\nreg_model = LinearRegression()\nreg_model.fit(df_reg.drop('Crime',axis=1), df_reg.Crime.values)\n\nLinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.LinearRegressionLinearRegression()\n\n\n\ny_pred = reg_model.predict(df_reg.drop('Crime',axis=1))\ny_true = df_reg.Crime.values\n\n\nplot_prediction_distribution(y_true,y_pred)\n\nMSE: 28828.633430503334\nRMSE: 169.789968580312\nMAE: 129.91521266409967", + "crumbs": [ + "chart_plotting" + ] + }, + { + "objectID": "chart_plotting.html#model-evaluation-curves", + "href": "chart_plotting.html#model-evaluation-curves", + "title": "chart_plotting", + "section": "Model evaluation curves", + "text": "Model evaluation curves\n\nsource\n\nplot_learning_curve\n\n plot_learning_curve (estimator, title, X, y, axes=None, ylim=None,\n cv=None, n_jobs=-1, scoring=None, train_sizes=[0.05,\n 0.24, 0.43, 0.62, 0.81, 1.0], save_fig=False,\n figsize=(20, 5), seed=42)\n\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nestimator\n\n\nsklearn’s classifier\n\n\ntitle\n\n\nTitle of the chart\n\n\nX\n\n\nTraining features\n\n\ny\n\n\nTraining label\n\n\naxes\nNoneType\nNone\nmatplotlib’s axes\n\n\nylim\nNoneType\nNone\ny axis range limit\n\n\ncv\nNoneType\nNone\nsklearn’s cross-validation splitting strategy\n\n\nn_jobs\nint\n-1\nNumber of jobs to run in parallel\n\n\nscoring\nNoneType\nNone\nmetric\n\n\ntrain_sizes\nlist\n[0.05, 0.24, 0.43, 0.62, 0.81, 1.0]\nList of training size portion\n\n\nsave_fig\nbool\nFalse\nTo store the chart as png in images directory\n\n\nfigsize\ntuple\n(20, 5)\nMatplotlib figsize\n\n\nseed\nint\n42\nRandom seed\n\n\n\n\ndt = DecisionTreeClassifier(criterion='entropy',random_state=42,min_samples_leaf=1)\nplot_learning_curve(dt,'Learning Curve - Decision Tree - Titanic',df_num.drop('Survived',axis=1),df_num['Survived'],\n cv=5,scoring='f1_macro',train_sizes=np.linspace(0.1,1,20))\n\n\n\n\n\n\n\n\n\nsource\n\n\nplot_validation_curve\n\n plot_validation_curve (estimator, title, X, y, ylim=None, cv=None,\n param_name=None, param_range=None, is_log=False,\n n_jobs=-1, scoring=None, save_fig=False,\n figsize=(8, 4), fill_between=True,\n enumerate_x=False)\n\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nestimator\n\n\nsklearn’s classifier\n\n\ntitle\n\n\nTitle of the chart\n\n\nX\n\n\nTraining features\n\n\ny\n\n\nTraining label\n\n\nylim\nNoneType\nNone\ny axis range limit\n\n\ncv\nNoneType\nNone\nsklearn’s cross-validation splitting strategy\n\n\nparam_name\nNoneType\nNone\nName of model’s hyperparameter\n\n\nparam_range\nNoneType\nNone\nList containing range of value for param_name\n\n\nis_log\nbool\nFalse\nTo log the value in param_range, for plotting\n\n\nn_jobs\nint\n-1\nNumber of jobs to run in parallel\n\n\nscoring\nNoneType\nNone\nmetric\n\n\nsave_fig\nbool\nFalse\nTo store the chart as png in images directory\n\n\nfigsize\ntuple\n(8, 4)\nMatplotlib figsize\n\n\nfill_between\nbool\nTrue\nTo add a upper and lower one-std line for train and test curve\n\n\nenumerate_x\nbool\nFalse\nConvert categorical hyperparam to numerical, for x axis\n\n\n\n\ndt = DecisionTreeClassifier(criterion='entropy',random_state=42)\nplot_validation_curve(dt,'Val Curve - Decision Tree - Titanic',df_num.drop('Survived',axis=1),df_num['Survived'],\n cv=5,param_range=np.arange(1,20,1),param_name='max_depth',scoring='f1_macro')", + "crumbs": [ + "chart_plotting" + ] + }, + { + "objectID": "chart_plotting.html#tree-visualization", + "href": "chart_plotting.html#tree-visualization", + "title": "chart_plotting", + "section": "Tree visualization", + "text": "Tree visualization\n\nsource\n\nplot_tree_dtreeviz\n\n plot_tree_dtreeviz (estimator, X, y, target_name:str,\n class_names:list=None, tree_index=0,\n depth_range_to_display=None, fancy=False, scale=1.0)\n\nPlot a decision tree using dtreeviz. Note that you need to install graphviz before using this function\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nestimator\n\n\nsklearn’s classifier\n\n\nX\n\n\nTraining features\n\n\ny\n\n\nTraining label\n\n\ntarget_name\nstr\n\nThe (string) name of the target variable; e.g., for Titanic, it’s “Survived”\n\n\nclass_names\nlist\nNone\nList of names associated with the labels (same order); e.g. [‘no’,‘yes’]\n\n\ntree_index\nint\n0\nIndex (from 0) of tree if model is an ensemble of trees like a random forest.\n\n\ndepth_range_to_display\nNoneType\nNone\nRange of depth levels to be displayed. The range values are inclusive\n\n\nfancy\nbool\nFalse\nTo draw fancy tree chart (as opposed to simplified one)\n\n\nscale\nfloat\n1.0\nScale of the chart. Higher means bigger\n\n\n\n\ndt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None,max_depth=3)\ndt.fit(df_num.drop('Survived',axis=1),df_num['Survived'])\n\nDecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=42)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.DecisionTreeClassifierDecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=42)\n\n\nAfter you have installed graphviz (https://github.com/parrt/dtreeviz#installation), run these codes to run dtreeviz\n\nplot_tree_dtreeviz(dt,df_num.drop('Survived',axis=1),df_num['Survived'],\n target_name='Survived',\n class_names=['no','yes'],\n fancy=True,scale=1)\n\n/home/quan/anaconda3/envs/dev/lib/python3.10/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n\n\n\n\n\n\n\n\n\n\nplot_tree_dtreeviz(dt,df_num.drop('Survived',axis=1),df_num['Survived'],\n target_name='Survived',\n class_names=['no','yes'],\n depth_range_to_display=[2,3],\n fancy=True,scale=1.2)\n\n/home/quan/anaconda3/envs/dev/lib/python3.10/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n\n\n\n\n\n\n\n\n\n\nsource\n\n\nplot_classification_tree_sklearn\n\n plot_classification_tree_sklearn (estimator, feature_names,\n class_names:list, rotate=True,\n fname='tmp')\n\nPlot a decision tree classifier using sklearn. Note that this will output a png file with fname instead of showing it in the notebook\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nestimator\n\n\nsklearn’s classifier\n\n\nfeature_names\n\n\nList of names of dependent variables (features)\n\n\nclass_names\nlist\n\nList of names associated with the labels (same order); e.g. [‘no’,‘yes’]\n\n\nrotate\nbool\nTrue\nTo rotate the tree graph\n\n\nfname\nstr\ntmp\nName of the png file to save(no extension)\n\n\n\n\n# feature names (not including label)\nfeature_names = df_num.drop('Survived',axis=1).columns.values\nprint(feature_names)\n\n['Pclass' 'Age' 'SibSp' 'Parch']\n\n\nAfter you have installed graphviz (https://github.com/parrt/dtreeviz#installation), run these codes to run sklearn tree plotting\ndt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None,max_depth=3)\ndt.fit(df_num.drop('Survived',axis=1),df_num['Survived'])\nplot_tree_sklearn(dt,feature_names=df_num.drop('Survived',axis=1).columns.values,\n class_names=['no','yes'],\n rotate=True,fname='tree_depth_3_titanic')\nTo show the image in notebook, create a markdown cell and type ![](images/tree_depth_3_titanic.png)", + "crumbs": [ + "chart_plotting" + ] + }, + { + "objectID": "chart_plotting.html#decision-trees-feature-importances", + "href": "chart_plotting.html#decision-trees-feature-importances", + "title": "chart_plotting", + "section": "Decision Tree’s feature importances", + "text": "Decision Tree’s feature importances\n\nsource\n\nplot_feature_importances\n\n plot_feature_importances (importances, feature_names, figsize=(20, 10),\n top_n=None)\n\nPlot and return a dataframe of feature importances, using sklearn’s feature_importances_ value\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nimportances\n\n\nfeature importances from sklearn’s feature_importances_ variable\n\n\nfeature_names\n\n\nList of names of dependent variables (features)\n\n\nfigsize\ntuple\n(20, 10)\nMatplotlib figsize\n\n\ntop_n\nNoneType\nNone\nShow top n features\n\n\n\n\nfeature_names = df_num.drop('Survived',axis=1).columns.values\n\n\ndt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None,max_depth=5)\ndt.fit(df_num.drop('Survived',axis=1),df_num['Survived'])\n\nplot_feature_importances(dt.feature_importances_,feature_names,top_n=3)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nImportance\n\n\nFeature\n\n\n\n\n\nPclass\n0.087868\n\n\nSibSp\n0.186577\n\n\nAge\n0.686647\n\n\n\n\n\n\n\n\n\nfeature_names = df_num.drop('Survived',axis=1).columns.values\ndt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None,max_depth=5)\ndt.fit(df_num.drop('Survived',axis=1),df_num['Survived'])\n\nplot_permutation_importances(dt,\n df_num.drop('Survived',axis=1),\n df_num['Survived'],\n scoring=['f1_macro','accuracy'],\n top_n=3)\n\nf1_macro\naccuracy\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n[ Importance STD\n Feature \n Pclass 0.049733 0.024178\n SibSp 0.063374 0.018909\n Age 0.126187 0.019904,\n Importance STD\n Feature \n Pclass 0.046154 0.014841\n SibSp 0.070513 0.013446\n Age 0.133333 0.021983]\n\n\n\nfeature_names = df_num.drop('Survived',axis=1).columns.values\ndt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None,max_depth=5)\ndt.fit(df_num.drop('Survived',axis=1),df_num['Survived'])\n\nplot_permutation_importances(dt,\n df_num.drop('Survived',axis=1),\n df_num['Survived'],\n scoring='f1_macro'\n )\n\nf1_macro\n\n\n\n\n\n\n\n\n\n[ Importance STD\n Feature \n Parch 0.002853 0.005706\n Pclass 0.049733 0.024178\n SibSp 0.063374 0.018909\n Age 0.126187 0.019904]", + "crumbs": [ + "chart_plotting" + ] + }, + { + "objectID": "chart_plotting.html#hyperparameters-visualization", + "href": "chart_plotting.html#hyperparameters-visualization", + "title": "chart_plotting", + "section": "Hyperparameters visualization", + "text": "Hyperparameters visualization\n\nsource\n\nparams_2D_heatmap\n\n params_2D_heatmap (search_cv:dict, param1:str, param2:str,\n scoring:str='f1_macro', log_param1=False,\n log_param2=False, figsize=(20, 10), min_hm=None,\n max_hm=None, higher_is_better=True)\n\nPlot 2D graph of metric value for each pair of hyperparameters\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nsearch_cv\ndict\n\nA dict with keys as column headers and values as columns. Typically an attribute (cv_results_) of GridSearchCV or RandomizedSearchCV\n\n\nparam1\nstr\n\nName of the first hyperparameter\n\n\nparam2\nstr\n\nName of the second hyperparameter\n\n\nscoring\nstr\nf1_macro\nMetric name\n\n\nlog_param1\nbool\nFalse\nTo log the first hyperparameter\n\n\nlog_param2\nbool\nFalse\nTo log the second hyperparameter\n\n\nfigsize\ntuple\n(20, 10)\nMatplotlib figsize\n\n\nmin_hm\nNoneType\nNone\nMinimum value for the metric to show\n\n\nmax_hm\nNoneType\nNone\nMaximum value of the metric to show\n\n\nhigher_is_better\nbool\nTrue\nSet if high metric is better\n\n\n\n\ndt = RandomForestClassifier(random_state=42)\nparam_grid={\n 'n_estimators': np.arange(2,20),\n 'min_samples_leaf': np.arange(1,80),\n}\n# Note: in order to use params_2D_heatmap, you should set scoring to a list, and set refit to False\nclf = RandomizedSearchCV(dt,param_grid,n_iter=100,\n scoring=['f1_macro'],n_jobs=-1,\n cv=5,verbose=1,random_state=42,refit=False)\nclf.fit(df_num.drop('Survived',axis=1),df_num['Survived'])\n\nFitting 5 folds for each of 100 candidates, totalling 500 fits\n\n\nRandomizedSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n n_iter=100, n_jobs=-1,\n param_distributions={'min_samples_leaf': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,\n 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79]),\n 'n_estimators': array([ 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n 19])},\n random_state=42, refit=False, scoring=['f1_macro'],\n verbose=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.RandomizedSearchCVRandomizedSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n n_iter=100, n_jobs=-1,\n param_distributions={'min_samples_leaf': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,\n 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79]),\n 'n_estimators': array([ 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n 19])},\n random_state=42, refit=False, scoring=['f1_macro'],\n verbose=1)estimator: RandomForestClassifierRandomForestClassifier(random_state=42)RandomForestClassifierRandomForestClassifier(random_state=42)\n\n\n\nparams_2D_heatmap(clf.cv_results_,'n_estimators','min_samples_leaf',\n scoring='f1_macro',\n figsize=(20,10))\n\n/tmp/ipykernel_4395/2399879344.py:14: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n\n\n\n\n\n\n\n\n\n\nparams_2D_heatmap(clf.cv_results_,'n_estimators','min_samples_leaf',\n scoring='f1_macro',\n figsize=(20,10),min_hm=0.45)\n\n/tmp/ipykernel_4395/2399879344.py:14: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n\n\n\n\n\n\n\n\n\n\ndt = RandomForestClassifier(random_state=42)\nparam_grid={\n 'n_estimators': np.arange(2,20),\n 'min_samples_leaf': np.arange(1,80),\n 'max_features': [0.3,0.4,0.5,0.6,0.7,0.8,0.9,1],\n}\n# Note: in order to use params_2D_heatmap, you should set scoring to a list, and set refit to False\nclf = RandomizedSearchCV(dt,param_grid,n_iter=100,\n scoring=['f1_macro'],n_jobs=-1,\n cv=5,verbose=1,random_state=42,refit=False)\nclf.fit(df_num.drop('Survived',axis=1),df_num['Survived'])\n\nFitting 5 folds for each of 100 candidates, totalling 500 fits\n\n\nRandomizedSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n n_iter=100, n_jobs=-1,\n param_distributions={'max_features': [0.3, 0.4, 0.5, 0.6,\n 0.7, 0.8, 0.9, 1],\n 'min_samples_leaf': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,\n 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79]),\n 'n_estimators': array([ 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n 19])},\n random_state=42, refit=False, scoring=['f1_macro'],\n verbose=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.RandomizedSearchCVRandomizedSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n n_iter=100, n_jobs=-1,\n param_distributions={'max_features': [0.3, 0.4, 0.5, 0.6,\n 0.7, 0.8, 0.9, 1],\n 'min_samples_leaf': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,\n 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79]),\n 'n_estimators': array([ 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n 19])},\n random_state=42, refit=False, scoring=['f1_macro'],\n verbose=1)estimator: RandomForestClassifierRandomForestClassifier(random_state=42)RandomForestClassifierRandomForestClassifier(random_state=42)\n\n\n\nparams_3D_heatmap(clf.cv_results_,\n 'n_estimators',\n 'min_samples_leaf',\n 'max_features',\n scoring='f1_macro')", + "crumbs": [ + "chart_plotting" + ] + }, + { + "objectID": "chart_plotting.html#partial-dependency-plot", + "href": "chart_plotting.html#partial-dependency-plot", + "title": "chart_plotting", + "section": "Partial Dependency Plot", + "text": "Partial Dependency Plot\n\nsource\n\npdp_numerical_only\n\n pdp_numerical_only (model, X:pandas.core.frame.DataFrame,\n num_features:list, class_names:list, y_colors=None,\n ncols=2, nrows=2, figsize=(20, 16))\n\nPlot PDP plot for numerical dependent variables\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nmodel\n\n\nsklearn tree model that has been trained\n\n\nX\npd.DataFrame\n\ndataframe to perform pdp\n\n\nnum_features\nlist\n\nA list of numerical features\n\n\nclass_names\nlist\n\nList of names associated with the labels (same order); e.g. [‘no’,‘yes’]\n\n\ny_colors\nNoneType\nNone\nList of colors associated with class_names\n\n\nncols\nint\n2\n\n\n\nnrows\nint\n2\n\n\n\nfigsize\ntuple\n(20, 16)\n\n\n\n\n\ndf = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/titanic.csv')\ndf = df[['Survived','Pclass','Sex','Age','SibSp','Parch','Embarked']].copy()\ndf = preprocessing_general(df,\n missing_cols=['Age','Embarked'],\n missing_vals=np.NaN,\n strategies=['median','most_frequent'],\n cat_cols='Embarked',\n bi_cols='Sex'\n )\n\n\ndf.head()\n\n\n\n\n\n\n\n\n\nSurvived\nPclass\nAge\nSibSp\nParch\nEmbarked_C\nEmbarked_Q\nEmbarked_S\nSex_male\n\n\n\n\n0\n0\n3\n22.0\n1\n0\nFalse\nFalse\nTrue\nTrue\n\n\n1\n1\n1\n38.0\n1\n0\nTrue\nFalse\nFalse\nFalse\n\n\n2\n1\n3\n26.0\n0\n0\nFalse\nFalse\nTrue\nFalse\n\n\n3\n1\n1\n35.0\n1\n0\nFalse\nFalse\nTrue\nFalse\n\n\n4\n0\n3\n35.0\n0\n0\nFalse\nFalse\nTrue\nTrue\n\n\n\n\n\n\n\n\nTo better showcase the interpretation of Partial Dependency Plot, we will reuse the Titanic dataset, but now the independent variable (the one we need to predict) will be Pclass (3 classes to predict)\n\nparams = {'n_estimators': 12, 'min_samples_leaf': 10, 'max_features': 0.8, 'class_weight': 'balanced'}\nX_trn = df.drop('Pclass',axis=1)\ny_trn = LabelEncoder().fit_transform(df['Pclass'])\ndt = RandomForestClassifier(**params)\ndt.fit(X_trn,y_trn)\n\nRandomForestClassifier(class_weight='balanced', max_features=0.8,\n min_samples_leaf=10, n_estimators=12)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.RandomForestClassifierRandomForestClassifier(class_weight='balanced', max_features=0.8,\n min_samples_leaf=10, n_estimators=12)\n\n\n\npdp_numerical_only(dt,X_trn,num_features=['Age','SibSp'],class_names=['pclass_1','pclass_2','pclass_3'],nrows=2,ncols=1,figsize=(6,8))\n\n\n\n\n\n\n\n\n\nsource\n\n\npdp_categorical_only\n\n pdp_categorical_only (model, X:pandas.core.frame.DataFrame,\n cat_feature:list, class_names:list, y_colors=None,\n ymax=0.5, figsize=(20, 8))\n\nPlot PDP plot for categorical dependent variables\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nmodel\n\n\nsklearn tree model that has been trained\n\n\nX\npd.DataFrame\n\ndataframe to perform pdp\n\n\ncat_feature\nlist\n\nA single categorical feature\n\n\nclass_names\nlist\n\nList of names associated with the labels (same order); e.g. [‘no’,‘yes’]\n\n\ny_colors\nNoneType\nNone\nList of colors associated with class_names\n\n\nymax\nfloat\n0.5\n\n\n\nfigsize\ntuple\n(20, 8)\n\n\n\n\n\npdp_categorical_only(dt,X_trn,'Survived',\n class_names=['pclass_1','pclass_2','pclass_3'])\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nsource\n\n\nplot_ice_pair\n\n plot_ice_pair (model, X:pandas.core.frame.DataFrame, pair_features:list,\n class_idx, figsize=(10, 4))\n\nPlot ICE plot from a pair of numerical feature\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nmodel\n\n\nsklearn tree model that has been trained\n\n\nX\npd.DataFrame\n\ndataframe to perform ice\n\n\npair_features\nlist\n\na list of only 2 features\n\n\nclass_idx\n\n\nindex of the class to plot\n\n\nfigsize\ntuple\n(10, 4)\n\n\n\n\n\n# For pclass_1\nplot_ice_pair(dt,X_trn,pair_features=['Age','SibSp'],class_idx=0,figsize=(8,3))\n\n\n\n\n\n\n\n\n\n# For pclass_2\nplot_ice_pair(dt,X_trn,pair_features=['Age','SibSp'],class_idx=1,figsize=(8,3))\n\n\n\n\n\n\n\n\n\n# For pclass_3\nplot_ice_pair(dt,X_trn,pair_features=['Age','SibSp'],class_idx=2,figsize=(8,3))", + "crumbs": [ + "chart_plotting" + ] + }, + { + "objectID": "chart_plotting.html#other-functions", + "href": "chart_plotting.html#other-functions", + "title": "chart_plotting", + "section": "Other functions", + "text": "Other functions\n\nsource\n\nplot_confusion_matrix\n\n plot_confusion_matrix (y_true:list|numpy.ndarray,\n y_pred:list|numpy.ndarray, labels=None)\n\nSimple function to plot the confusion matrix\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\ny_true\nlist | np.ndarray\n\nA list/numpy array of true labels\n\n\ny_pred\nlist | np.ndarray\n\nA list/numpy array of predictions\n\n\nlabels\nNoneType\nNone\nDisplay names matching the labels (same order).\n\n\n\n\ny_true = np.array([1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0])\ny_pred = np.array([1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0])\n\n\nplot_confusion_matrix(y_true,y_pred,labels=['Not Survived','Survived'])", + "crumbs": [ + "chart_plotting" + ] + }, + { + "objectID": "index.html", + "href": "index.html", + "title": "that-ml-library", + "section": "", + "text": "pip install that_ml_library\nFor tree visualization function (plot_tree_dtreeviz or plot_tree_sklearn), you also need to install graphviz. Please follow the instruction here", + "crumbs": [ + "that-ml-library" + ] + }, + { + "objectID": "index.html#install", + "href": "index.html#install", + "title": "that-ml-library", + "section": "", + "text": "pip install that_ml_library\nFor tree visualization function (plot_tree_dtreeviz or plot_tree_sklearn), you also need to install graphviz. Please follow the instruction here", + "crumbs": [ + "that-ml-library" + ] + }, + { + "objectID": "index.html#how-to-use", + "href": "index.html#how-to-use", + "title": "that-ml-library", + "section": "How to use", + "text": "How to use\nPlease visit https://anhquan0412.github.io/that-ml-library/ for tutorials and documentations", + "crumbs": [ + "that-ml-library" + ] + }, + { + "objectID": "index.html#a-word-of-caution", + "href": "index.html#a-word-of-caution", + "title": "that-ml-library", + "section": "A word of caution", + "text": "A word of caution\nThis library should only be utilized solely for developing a proof of concept or prototype for your machine learning model with your specific dataset, with the aim of evaluating the model’s performance and interpretability. For deployment in a production environment, opt for a more organized methodology, such as https://scikit-learn.org/stable/modules/compose.html#pipeline", + "crumbs": [ + "that-ml-library" + ] + }, + { + "objectID": "data_preprocess.html", + "href": "data_preprocess.html", + "title": "data_preprocess", + "section": "", + "text": "source\n\nprocess_missing_values\n\n process_missing_values (X_train:pandas.core.frame.DataFrame,\n X_test:pandas.core.frame.DataFrame=None,\n missing_cols:Union[list,str]=[],\n missing_vals:Union[list,int,float,str]=nan,\n strategies:Union[list,str]='median', **kwargs)\n\nProcess columns with missing values using Sklearn SimpleInputer\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nX_train\npd.DataFrame\n\nTraining dataframe\n\n\nX_test\npd.DataFrame\nNone\nTesting dataframe\n\n\nmissing_cols\nlist | str\n[]\nA column name having missing values, or a list of such columns\n\n\nmissing_vals\nlist | int | float | str\nnan\nA placeholder for missing values, or a list of placeholders for all columns in miss_cols\n\n\nstrategies\nlist | str\nmedian\nThe imputation strategy from sklearn, or a list of such values. Currently support ‘median’,‘mean’,‘most_frequent’\n\n\nkwargs\n\n\n\n\n\n\n\ndf = pd.DataFrame([[7, 2, 3], [4, np.nan, 6], [10, 5, -1]],columns=['col1','col2','col3'])\ndisplay(df)\n\n\n\n\n\n\n\n\n\ncol1\ncol2\ncol3\n\n\n\n\n0\n7\n2.0\n3\n\n\n1\n4\nNaN\n6\n\n\n2\n10\n5.0\n-1\n\n\n\n\n\n\n\n\n\ndf_processed = process_missing_values(df,missing_cols=['col2','col3'],missing_vals=[np.NaN,-1],strategy='mean')\ndisplay(df_processed)\n\n\n\n\n\n\n\n\n\ncol1\ncol2\ncol3\n\n\n\n\n0\n7\n2.0\n3.0\n\n\n1\n4\n3.5\n6.0\n\n\n2\n10\n5.0\n4.5\n\n\n\n\n\n\n\n\n\ndf_trn = pd.DataFrame([[7, 2, 3], [4, np.nan, 6], [10, 5, -1]],columns=['col1','col2','col3'])\ndf_test = pd.DataFrame([[2, np.NaN, 3], [3, 1, -1]],columns=['col1','col2','col3'])\ndisplay(df_trn,df_test)\n\n\n\n\n\n\n\n\n\ncol1\ncol2\ncol3\n\n\n\n\n0\n7\n2.0\n3\n\n\n1\n4\nNaN\n6\n\n\n2\n10\n5.0\n-1\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ncol1\ncol2\ncol3\n\n\n\n\n0\n2\nNaN\n3\n\n\n1\n3\n1.0\n-1\n\n\n\n\n\n\n\n\n\ndf_processed_trn,df_procesed_val= process_missing_values(df_trn,\n df_test,\n missing_cols=['col2','col3'],\n missing_vals=[np.NaN,-1],strategy='mean')\ndisplay(df_processed_trn,df_procesed_val)\n\n\n\n\n\n\n\n\n\ncol1\ncol2\ncol3\n\n\n\n\n0\n7\n2.0\n3.0\n\n\n1\n4\n3.5\n6.0\n\n\n2\n10\n5.0\n4.5\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ncol1\ncol2\ncol3\n\n\n\n\n0\n2\n3.5\n3.0\n\n\n1\n3\n1.0\n4.5\n\n\n\n\n\n\n\n\n\nsource\n\n\nscale_num_cols\n\n scale_num_cols (X_train:pandas.core.frame.DataFrame,\n X_test:pandas.core.frame.DataFrame=None,\n num_cols:Union[list,str]=[],\n scale_methods:Union[list,str]='minmax', **kwargs)\n\nScale numerical columns using Sklearn\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nX_train\npd.DataFrame\n\nTraining dataframe\n\n\nX_test\npd.DataFrame\nNone\nTesting dataframe\n\n\nnum_cols\nlist | str\n[]\nName of the numerical column, or a list of such columns\n\n\nscale_methods\nlist | str\nminmax\nSklearn scaling method (‘minmax’ or ‘standard’), or a list of such methods\n\n\nkwargs\n\n\n\n\n\n\n\ndf = pd.DataFrame([[7, 2, 3], [4, 2, 6], [10, 5, 1]],columns=['col1','col2','col3'])\ndisplay(df)\n\n\n\n\n\n\n\n\n\ncol1\ncol2\ncol3\n\n\n\n\n0\n7\n2\n3\n\n\n1\n4\n2\n6\n\n\n2\n10\n5\n1\n\n\n\n\n\n\n\n\n\ndf_processed = scale_num_cols(df,num_cols=['col1','col3'],scale_methods='standard')\ndisplay(df_processed)\n\n\n\n\n\n\n\n\n\ncol1\ncol2\ncol3\n\n\n\n\n0\n0.000000\n2\n-0.162221\n\n\n1\n-1.224745\n2\n1.297771\n\n\n2\n1.224745\n5\n-1.135550\n\n\n\n\n\n\n\n\n\nsource\n\n\none_hot_cat\n\n one_hot_cat (X_train:pandas.core.frame.DataFrame,\n X_test:pandas.core.frame.DataFrame=None,\n cat_cols:Union[list,str]=[], bi_cols:Union[list,str]=[],\n **kwargs)\n\nPerform ‘get_dummies’ on categorical columns\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nX_train\npd.DataFrame\n\nTraining dataframe\n\n\nX_test\npd.DataFrame\nNone\nTesting dataframe\n\n\ncat_cols\nlist | str\n[]\nName of the categorical columns (non-binary), or a list of such columns\n\n\nbi_cols\nlist | str\n[]\nName of the binary column, or a list of such columns\n\n\nkwargs\n\n\n\n\n\n\n\ndf = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],\n 'C': [1, 2, 3]})\ndisplay(df)\n\n\n\n\n\n\n\n\n\nA\nB\nC\n\n\n\n\n0\na\nb\n1\n\n\n1\nb\na\n2\n\n\n2\na\nc\n3\n\n\n\n\n\n\n\n\n\ndf_processed = one_hot_cat(df,cat_cols='B',bi_cols='A')\ndisplay(df_processed)\n\n\n\n\n\n\n\n\n\nC\nB_a\nB_b\nB_c\nA_b\n\n\n\n\n0\n1\n0.0\n1.0\n0.0\n0.0\n\n\n1\n2\n1.0\n0.0\n0.0\n1.0\n\n\n2\n3\n0.0\n0.0\n1.0\n0.0\n\n\n\n\n\n\n\n\n\nsource\n\n\npreprocessing_general\n\n preprocessing_general (X_train:pandas.core.frame.DataFrame,\n X_test:pandas.core.frame.DataFrame=None, **kwargs)\n\nThe main preprocessing functions, will perform:\n\nFill missing values\nScale numerical columns\nOne-hot encode categorical columns\n\nRemember to put in the appropriate keyword arguments for each of the preprocessings mentioned above\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nX_train\npd.DataFrame\n\nTraining dataframe\n\n\nX_test\npd.DataFrame\nNone\nTesting dataframe\n\n\nkwargs\n\n\n\n\n\n\n\ndf = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/titanic.csv')\n\n\n# Select some useful features, for now\ndf = df[['Survived','Pclass','Sex','Age','SibSp','Parch','Embarked']].copy()\ndf.info()\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 156 entries, 0 to 155\nData columns (total 7 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Survived 156 non-null int64 \n 1 Pclass 156 non-null int64 \n 2 Sex 156 non-null object \n 3 Age 126 non-null float64\n 4 SibSp 156 non-null int64 \n 5 Parch 156 non-null int64 \n 6 Embarked 155 non-null object \ndtypes: float64(1), int64(4), object(2)\nmemory usage: 8.7+ KB\n\n\n\ndf.sample(5)\n\n\n\n\n\n\n\n\n\nSurvived\nPclass\nSex\nAge\nSibSp\nParch\nEmbarked\n\n\n\n\n84\n1\n2\nfemale\n17.0\n0\n0\nS\n\n\n4\n0\n3\nmale\n35.0\n0\n0\nS\n\n\n101\n0\n3\nmale\nNaN\n0\n0\nS\n\n\n48\n0\n3\nmale\nNaN\n2\n0\nC\n\n\n112\n0\n3\nmale\n22.0\n0\n0\nS\n\n\n\n\n\n\n\n\nLet’s perform a simple train/test split\n\nfrom sklearn.model_selection import train_test_split\n\n\nX_train, X_test, y_train, y_test = train_test_split(df.drop('Survived',axis=1), df['Survived'],\n test_size=0.2,\n random_state=42,\n stratify=df['Survived'])\n\n\nX_train.head()\n\n\n\n\n\n\n\n\n\nPclass\nSex\nAge\nSibSp\nParch\nEmbarked\n\n\n\n\n142\n3\nfemale\n24.0\n1\n0\nS\n\n\n134\n2\nmale\n25.0\n0\n0\nS\n\n\n120\n2\nmale\n21.0\n2\n0\nS\n\n\n50\n3\nmale\n7.0\n4\n1\nS\n\n\n133\n2\nfemale\n29.0\n1\n0\nS\n\n\n\n\n\n\n\n\n\nX_test.head()\n\n\n\n\n\n\n\n\n\nPclass\nSex\nAge\nSibSp\nParch\nEmbarked\n\n\n\n\n91\n3\nmale\n20.0\n0\n0\nS\n\n\n145\n2\nmale\n19.0\n1\n1\nS\n\n\n115\n3\nmale\n21.0\n0\n0\nS\n\n\n106\n3\nfemale\n21.0\n0\n0\nS\n\n\n9\n2\nfemale\n14.0\n1\n0\nC\n\n\n\n\n\n\n\n\n\nX_train_processed,X_test_processed = preprocessing_general(X_train,X_test,\n missing_cols=['Age','Embarked'],\n missing_vals=np.NaN,\n strategies=['median','most_frequent'],\n num_cols=['Age','SibSp','Parch'],\n scale_methods=['standard','minmax','minmax'],\n cat_cols='Embarked',\n bi_cols='Sex'\n )\n\nNotice that I don’t add Pclass to the preprocessing function. That means this column will be left untouched\n\nX_train_processed.head()\n\n\n\n\n\n\n\n\n\nPclass\nAge\nSibSp\nParch\nEmbarked_C\nEmbarked_Q\nEmbarked_S\nSex_male\n\n\n\n\n142\n3\n-0.325526\n0.2\n0.0\n0.0\n0.0\n1.0\n0.0\n\n\n134\n2\n-0.252796\n0.0\n0.0\n0.0\n0.0\n1.0\n1.0\n\n\n120\n2\n-0.543716\n0.4\n0.0\n0.0\n0.0\n1.0\n1.0\n\n\n50\n3\n-1.561938\n0.8\n0.2\n0.0\n0.0\n1.0\n1.0\n\n\n133\n2\n0.038125\n0.2\n0.0\n0.0\n0.0\n1.0\n0.0\n\n\n\n\n\n\n\n\n\nX_test_processed.head()\n\n\n\n\n\n\n\n\n\nPclass\nAge\nSibSp\nParch\nEmbarked_C\nEmbarked_Q\nEmbarked_S\nSex_male\n\n\n\n\n91\n3\n-0.616446\n0.0\n0.0\n0.0\n0.0\n1.0\n1.0\n\n\n145\n2\n-0.689176\n0.2\n0.2\n0.0\n0.0\n1.0\n1.0\n\n\n115\n3\n-0.543716\n0.0\n0.0\n0.0\n0.0\n1.0\n1.0\n\n\n106\n3\n-0.543716\n0.0\n0.0\n0.0\n0.0\n1.0\n0.0\n\n\n9\n2\n-1.052827\n0.2\n0.0\n1.0\n0.0\n0.0\n0.0", + "crumbs": [ + "data_preprocess" + ] + }, + { + "objectID": "utils.html", + "href": "utils.html", + "title": "utils", + "section": "", + "text": "source\n\nval2list\n\n val2list (val, lsize=1)\n\nConvert an element (nonlist value) to a list of 1 element\n\nval2list(2)\n\n[2]\n\n\n\nval2list(2,lsize=5)\n\n[2, 2, 2, 2, 2]\n\n\n\nval2list([2,3])\n\n[2, 3]\n\n\n\nval2list(np.array([1,2,3]))\n\narray([1, 2, 3])\n\n\n\nsource\n\n\ncreate_dir\n\n create_dir (path_dir)", + "crumbs": [ + "utils" + ] + }, + { + "objectID": "end_to_end_example.html", + "href": "end_to_end_example.html", + "title": "End-to-end tutorial - Classification", + "section": "", + "text": "In this tutorial, we will use the Pima Indian dataset https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.\nfrom that_ml_library.data_preprocess import *\nfrom that_ml_library.chart_plotting import *\nfrom that_ml_library.ml_helpers import *", + "crumbs": [ + "End-to-end tutorial - Classification" + ] + }, + { + "objectID": "end_to_end_example.html#load-data", + "href": "end_to_end_example.html#load-data", + "title": "End-to-end tutorial - Classification", + "section": "Load data", + "text": "Load data\n\nimport pandas as pd\nimport numpy as np\n\n\ndf = pd.read_csv('https://raw.githubusercontent.com/anhquan0412/dataset/main/diabetes.csv')\nprint(df.shape)\ndisplay(df.sample(7))\n\n(768, 9)\n\n\n\n\n\n\n\n\n\n\nPregnancies\nGlucose\nBloodPressure\nSkinThickness\nInsulin\nBMI\nDiabetesPedigreeFunction\nAge\nOutcome\n\n\n\n\n92\n7\n81\n78\n40\n48\n46.7\n0.261\n42\n0\n\n\n54\n7\n150\n66\n42\n342\n34.7\n0.718\n42\n0\n\n\n288\n4\n96\n56\n17\n49\n20.8\n0.340\n26\n0\n\n\n90\n1\n80\n55\n0\n0\n19.1\n0.258\n21\n0\n\n\n98\n6\n93\n50\n30\n64\n28.7\n0.356\n23\n0\n\n\n397\n0\n131\n66\n40\n0\n34.3\n0.196\n22\n1\n\n\n574\n1\n143\n86\n30\n330\n30.1\n0.892\n23\n0\n\n\n\n\n\n\n\n\n\ndf.info()\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 768 entries, 0 to 767\nData columns (total 9 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Pregnancies 768 non-null int64 \n 1 Glucose 768 non-null int64 \n 2 BloodPressure 768 non-null int64 \n 3 SkinThickness 768 non-null int64 \n 4 Insulin 768 non-null int64 \n 5 BMI 768 non-null float64\n 6 DiabetesPedigreeFunction 768 non-null float64\n 7 Age 768 non-null int64 \n 8 Outcome 768 non-null int64 \ndtypes: float64(2), int64(7)\nmemory usage: 54.1 KB", + "crumbs": [ + "End-to-end tutorial - Classification" + ] + }, + { + "objectID": "end_to_end_example.html#sklearn-logistic-regression", + "href": "end_to_end_example.html#sklearn-logistic-regression", + "title": "End-to-end tutorial - Classification", + "section": "Sklearn Logistic Regression", + "text": "Sklearn Logistic Regression\n\ndf_train = X_train_processed.copy()\ndf_train['has_diabetes'] = y_train\n\n\nget_vif(df_train,plot_corr=True)\n\n\n\n\n\n\n\n\nconst 40.937975\nPregnancies 1.441405\nGlucose 1.573457\nBloodPressure 1.152426\nSkinThickness 1.529585\nInsulin 1.538192\nBMI 1.343058\nDiabetesPedigreeFunction 1.097619\nAge 1.577323\nhas_diabetes 1.451467\ndtype: float64\n\n\nWith VIF, we can see that there’s not much colinearity in this dataset. Note that VIF >5 or >10 means high colinearity\n\nrun_logistic_regression(X_train_processed,y_train,\n multi_class='multinomial',\n solver='newton-cg',max_iter=10000)\n\n\n\n\n\n\n\n\n\nFeatures\nCoefficients\nCoefficients Exp\n\n\n\n\n0\nIntercept\n-4.233888\n0.014496\n\n\n1\nPregnancies\n0.976679\n2.655623\n\n\n2\nGlucose\n3.679170\n39.613487\n\n\n3\nBloodPressure\n-0.679500\n0.506870\n\n\n4\nSkinThickness\n0.217158\n1.242540\n\n\n5\nInsulin\n-0.473694\n0.622698\n\n\n6\nBMI\n3.106698\n22.347132\n\n\n7\nDiabetesPedigreeFunction\n0.881984\n2.415688\n\n\n8\nAge\n0.461467\n1.586400\n\n\n\n\n\n\n\n\n----------------------------------------------------------------------------------------------------\nLog loss: 0.46620685663024525\n----------------------------------------------------------------------------------------------------\n precision recall f1-score support\n\n 0 0.81 0.90 0.85 400\n 1 0.76 0.60 0.67 214\n\n accuracy 0.79 614\n macro avg 0.78 0.75 0.76 614\nweighted avg 0.79 0.79 0.79 614\n\n\n\nLogisticRegression(max_iter=10000, multi_class='multinomial', penalty=None,\n random_state=0, solver='newton-cg')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.LogisticRegressionLogisticRegression(max_iter=10000, multi_class='multinomial', penalty=None,\n random_state=0, solver='newton-cg')\n\n\nInterpretation of coefficients in logistic regression: For a coefficient β1, given a one unit increase in one of the variables (say X1), the odds of X (p(X)/1-p(X)) will be multiplied by e^β1.\nIn this case, we can tell from coefficient of Glucose that: the odd of having diabetes will be multiplied by ~40 if Glucose level is increased by 1\n\nStatsmodel\n\nrun_multinomial_statmodel(X_train_processed,y_train,add_constant=True)\n\nOptimization terminated successfully.\n Current function value: 0.466207\n Iterations 6\n MNLogit Regression Results \n==============================================================================\nDep. Variable: Outcome No. Observations: 614\nModel: MNLogit Df Residuals: 605\nMethod: MLE Df Model: 8\nDate: Mon, 19 Feb 2024 Pseudo R-squ.: 0.2789\nTime: 11:43:30 Log-Likelihood: -286.25\nconverged: True LL-Null: -396.97\nCovariance Type: nonrobust LLR p-value: 1.909e-43\n============================================================================================\n Outcome=1 coef std err z P>|z| [0.025 0.975]\n--------------------------------------------------------------------------------------------\nconst -8.4678 0.805 -10.523 0.000 -10.045 -6.891\nPregnancies 1.9534 0.624 3.129 0.002 0.730 3.177\nGlucose 7.3584 0.855 8.609 0.000 5.683 9.034\nBloodPressure -1.3590 0.733 -1.855 0.064 -2.795 0.077\nSkinThickness 0.4343 0.788 0.551 0.581 -1.110 1.978\nInsulin -0.9474 0.856 -1.107 0.268 -2.625 0.730\nBMI 6.2135 1.150 5.405 0.000 3.960 8.467\nDiabetesPedigreeFunction 1.7640 0.755 2.335 0.020 0.283 3.245\nAge 0.9229 0.616 1.499 0.134 -0.284 2.130\n============================================================================================\n----------------------------------------------------------------------------------------------------\nLog loss: 0.46620685662395556\n----------------------------------------------------------------------------------------------------\n precision recall f1-score support\n\n 0 0.81 0.90 0.85 400\n 1 0.76 0.60 0.67 214\n\n accuracy 0.79 614\n macro avg 0.78 0.75 0.76 614\nweighted avg 0.79 0.79 0.79 614\n\n\n\n<statsmodels.discrete.discrete_model.MNLogit>", + "crumbs": [ + "End-to-end tutorial - Classification" + ] + }, + { + "objectID": "end_to_end_example.html#decision-tree", + "href": "end_to_end_example.html#decision-tree", + "title": "End-to-end tutorial - Classification", + "section": "Decision Tree", + "text": "Decision Tree\n\nValidation Curve\n\nfrom sklearn.tree import DecisionTreeClassifier\n\n\ndt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None)\ncv = StratifiedKFold(5)\nplot_validation_curve(dt,'Val Curve - Decision Tree - PID',X_train_processed,y_train,\n cv=cv,param_range=np.arange(1,20,1),param_name='max_depth',scoring='f1_macro',\n n_jobs=-1,figsize=(6,4))\n\n\n\n\n\n\n\n\nThis is the validation curve for hyperparameter max depth, which can be used to prune a decision tree.\nFor max depth, we can observe the overfitting behavior when max depth is too high, which means the tree is able to make unlimited splits to the extreme of classifying each of the training data points correctly, thus failing to generalize. On the other hand, a small depth is enough to achieve a good CV score (from depth 1 to 5). This is a telling sign that some features have strong signals to predict diabetes than others.\n\n\nLearning curve\n\nnp.linspace(0.1,1,20)\n\narray([0.1 , 0.14736842, 0.19473684, 0.24210526, 0.28947368,\n 0.33684211, 0.38421053, 0.43157895, 0.47894737, 0.52631579,\n 0.57368421, 0.62105263, 0.66842105, 0.71578947, 0.76315789,\n 0.81052632, 0.85789474, 0.90526316, 0.95263158, 1. ])\n\n\n\ndt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None,min_samples_leaf=27)\nplot_learning_curve(dt,'DT Learning Curve',X_train_processed,y_train,cv=cv,scoring='f1_macro',\n train_sizes=np.linspace(0.1,1,20))\n\n\n\n\n\n\n\n\nBased on the learning curve for the best tuned decision tree, more data is better for the model to learn, as training score seems to convert, and CV score still slowly increases. However, as the gap between CV score and train score is closing, adding lots of data might not be beneficial. For the scalability of the model, the relationship between training time and fit time is linear, which makes sense as more data allows tree to grow more, which takes more time to fit and predict.\n\n\nTree plotting\nUsing Dtreeviz\n\ndt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None,max_depth=3)\ndt.fit(X_train_processed,y_train)\nplot_tree_dtreeviz(dt,X_train_processed,y_train,target_name='has_diabetes',class_names=['no','yes'],\n fancy=True,scale=1.0)\n\n/home/quan/anaconda3/envs/dev/lib/python3.10/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n\n\n\n\n\n\n\n\n\nGlucose alone can make CV F1 score to reach 0.704, which is not far from the CV score of the best tuned model. With domain knowledge, we can confirm that high glucose is the number one indicator for diabetes, because diabetes patients are unable to effectively use insulin to break down glucose.\nUsing Sklearn tree plot\n\ndt = DecisionTreeClassifier(criterion='entropy',random_state=42,class_weight=None,max_depth=3)\ndt.fit(X_train_processed,y_train)\nplot_classification_tree_sklearn(dt,feature_names=X_train_processed.columns.values,\n class_names=['no','yes'],\n rotate=True,fname='tree_depth_3_PID')\n\nType ![](images/tree_depth_3_PID.png)", + "crumbs": [ + "End-to-end tutorial - Classification" + ] + }, + { + "objectID": "end_to_end_example.html#model-finetuning-process", + "href": "end_to_end_example.html#model-finetuning-process", + "title": "End-to-end tutorial - Classification", + "section": "Model finetuning process", + "text": "Model finetuning process\n\nfrom sklearn.ensemble import RandomForestClassifier\n\n\nparam_grid = {\n 'n_estimators': np.arange(2,80),\n 'min_samples_leaf': np.arange(1,100),\n 'max_features': [0.3,0.4,0.5,0.6,0.7,0.8,0.9,1],\n}\nsearch_cv = tune_sklearn_model('RandomForest',param_grid,X_train_processed,y_train,\n is_regression=False,\n custom_cv=5,\n scoring='f1_macro',\n random_cv_iter=100,\n rank_show=7,\n show_split_scores=True)\n\nFitting 5 folds for each of 100 candidates, totalling 500 fits\n----------\nRank 1\nParams: {'n_estimators': 4, 'min_samples_leaf': 23, 'max_features': 0.7}\nTrain scores: [0.77, 0.76, 0.78, 0.76, 0.78]\nMean train score: 0.768 +- 0.008\nTest scores: [0.73, 0.77, 0.71, 0.7, 0.77]\nMean test score: 0.738 +- 0.029\n----------\nRank 2\nParams: {'n_estimators': 41, 'min_samples_leaf': 5, 'max_features': 0.5}\nTrain scores: [0.88, 0.87, 0.86, 0.88, 0.87]\nMean train score: 0.872 +- 0.009\nTest scores: [0.71, 0.78, 0.71, 0.71, 0.77]\nMean test score: 0.737 +- 0.031\n----------\nRank 3\nParams: {'n_estimators': 31, 'min_samples_leaf': 10, 'max_features': 0.6}\nTrain scores: [0.83, 0.81, 0.82, 0.82, 0.81]\nMean train score: 0.819 +- 0.011\nTest scores: [0.72, 0.77, 0.7, 0.69, 0.79]\nMean test score: 0.737 +- 0.040\n----------\nRank 4\nParams: {'n_estimators': 20, 'min_samples_leaf': 28, 'max_features': 0.5}\nTrain scores: [0.77, 0.76, 0.78, 0.76, 0.78]\nMean train score: 0.770 +- 0.008\nTest scores: [0.75, 0.76, 0.71, 0.68, 0.78]\nMean test score: 0.735 +- 0.037\n----------\nRank 5\nParams: {'n_estimators': 18, 'min_samples_leaf': 94, 'max_features': 0.7}\nTrain scores: [0.73, 0.73, 0.74, 0.75, 0.72]\nMean train score: 0.735 +- 0.011\nTest scores: [0.74, 0.76, 0.7, 0.71, 0.77]\nMean test score: 0.735 +- 0.026\n----------\nRank 6\nParams: {'n_estimators': 35, 'min_samples_leaf': 61, 'max_features': 0.8}\nTrain scores: [0.74, 0.74, 0.77, 0.75, 0.73]\nMean train score: 0.746 +- 0.011\nTest scores: [0.8, 0.76, 0.69, 0.67, 0.76]\nMean test score: 0.734 +- 0.045\n----------\nRank 7\nParams: {'n_estimators': 35, 'min_samples_leaf': 8, 'max_features': 0.5}\nTrain scores: [0.84, 0.82, 0.83, 0.84, 0.82]\nMean train score: 0.832 +- 0.008\nTest scores: [0.75, 0.76, 0.7, 0.71, 0.76]\nMean test score: 0.734 +- 0.027\n----------\nDefault Params\nMean train score: 1.0 +- 0.0\nMean test score: 0.731 +- 0.04\n\n\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 24 concurrent workers.\n[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 0.2s finished\n\n\n\nparams_3D_heatmap(search_cv,'n_estimators','min_samples_leaf','max_features','f1_macro')\n\n \n\n\nBy looking at the 3D map of these three hyperparameters, we might need to exclude max_features of 1 and min_samples_leaf that is more than 80. In fact, max_features of 0.8 and 0.9 shows the most promising set of hyperparameters, thus we can now simplied our search space to this:\n\nparam_grid = {\n 'n_estimators': np.arange(2,80),\n 'min_samples_leaf': np.arange(1,85),\n 'max_features': [0.8,0.9],\n}\nsearch_cv = tune_sklearn_model('RandomForest',param_grid,X_train_processed,y_train,custom_cv=5,\n scoring='f1_macro',\n random_cv_iter=200,\n rank_show=2,\n show_split_scores=True)\n\nFitting 5 folds for each of 200 candidates, totalling 1000 fits\n----------\nRank 1\nParams: {'n_estimators': 12, 'min_samples_leaf': 12, 'max_features': 0.8}\nTrain scores: [0.8, 0.8, 0.81, 0.82, 0.8]\nMean train score: 0.806 +- 0.010\nTest scores: [0.74, 0.79, 0.7, 0.7, 0.79]\nMean test score: 0.743 +- 0.041\n----------\nRank 2\nParams: {'n_estimators': 33, 'min_samples_leaf': 23, 'max_features': 0.8}\nTrain scores: [0.78, 0.76, 0.77, 0.77, 0.76]\nMean train score: 0.769 +- 0.007\nTest scores: [0.75, 0.77, 0.71, 0.69, 0.79]\nMean test score: 0.742 +- 0.038\n----------\nDefault Params\nMean train score: 1.0 +- 0.0\nMean test score: 0.731 +- 0.04\n\n\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 24 concurrent workers.\n[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 0.2s finished\n\n\n\nparams_2D_heatmap(search_cv,'n_estimators','min_samples_leaf','f1_macro',\n figsize=(20,10),min_hm=0.7,max_hm=None)\n\n\n\n\n\n\n\n\nWe will now focus on the area near the bottom right of the graph, for grid search\n\nparam_grid = {\n 'n_estimators': np.arange(62,73),\n 'min_samples_leaf': np.arange(20,26),\n 'max_features': [0.9],\n}\nsearch_cv = tune_sklearn_model('RandomForest',\n param_grid,\n X_train_processed,\n y_train,\n scoring='f1_macro',\n custom_cv=5,\n rank_show=5)\n\nFitting 5 folds for each of 66 candidates, totalling 330 fits\n----------\nRank 1\nParams: {'max_features': 0.9, 'min_samples_leaf': 25, 'n_estimators': 72}\nTrain scores: [0.77, 0.75, 0.78, 0.76, 0.77]\nMean train score: 0.767 +- 0.009\nTest scores: [0.74, 0.78, 0.7, 0.72, 0.78]\nMean test score: 0.745 +- 0.033\n----------\nRank 1\nParams: {'max_features': 0.9, 'min_samples_leaf': 25, 'n_estimators': 63}\nTrain scores: [0.78, 0.75, 0.77, 0.77, 0.77]\nMean train score: 0.765 +- 0.009\nTest scores: [0.74, 0.78, 0.7, 0.72, 0.78]\nMean test score: 0.745 +- 0.033\n----------\nRank 1\nParams: {'max_features': 0.9, 'min_samples_leaf': 25, 'n_estimators': 65}\nTrain scores: [0.78, 0.75, 0.78, 0.76, 0.77]\nMean train score: 0.766 +- 0.009\nTest scores: [0.74, 0.78, 0.7, 0.72, 0.78]\nMean test score: 0.745 +- 0.033\n----------\nRank 1\nParams: {'max_features': 0.9, 'min_samples_leaf': 25, 'n_estimators': 62}\nTrain scores: [0.77, 0.75, 0.77, 0.77, 0.77]\nMean train score: 0.765 +- 0.008\nTest scores: [0.74, 0.78, 0.7, 0.72, 0.78]\nMean test score: 0.745 +- 0.033\n----------\nRank 5\nParams: {'max_features': 0.9, 'min_samples_leaf': 25, 'n_estimators': 67}\nTrain scores: [0.78, 0.75, 0.77, 0.76, 0.77]\nMean train score: 0.766 +- 0.011\nTest scores: [0.74, 0.78, 0.7, 0.71, 0.78]\nMean test score: 0.743 +- 0.034\n----------\nDefault Params\nMean train score: 1.0 +- 0.0\nMean test score: 0.731 +- 0.04\n\n\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 24 concurrent workers.\n[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 0.2s finished", + "crumbs": [ + "End-to-end tutorial - Classification" + ] + }, + { + "objectID": "end_to_end_example.html#feature-importance-permutation-technique", + "href": "end_to_end_example.html#feature-importance-permutation-technique", + "title": "End-to-end tutorial - Classification", + "section": "Feature importance (permutation technique)", + "text": "Feature importance (permutation technique)\n\nparams= {'max_features': 0.9, 'min_samples_leaf': 25, 'n_estimators': 62}\n# Mean train score: 0.765 +- 0.008\n# Mean test score: 0.745 +- 0.033\n\n\n# # turn off warnings\n# def warn(*args, **kwargs):\n# pass\n# import warnings\n# warnings.warn = warn\n\n\nfinal_model= run_sklearn_model('RandomForest',\n params,\n X_train_processed,y_train,\n is_regression=False,\n class_names=['no','yes'],\n test_split=None,\n seed=42,\n plot_fea_imp=True)\n\n------------------------------ Train set ------------------------------\nLog loss: 0.4134397147185217\n precision recall f1-score support\n\n no 0.82 0.90 0.86 400\n yes 0.78 0.63 0.70 214\n\n accuracy 0.81 614\n macro avg 0.80 0.77 0.78 614\nweighted avg 0.80 0.81 0.80 614\n\n\n\n\n\n\n\n\n\n\nInsulin is not an impact feature for this dataset. Digging deeper on the source of the data, there’s a description that said: “This population has minimal European admixture, and their diabetes appears to be exclusively type 2 diabetes, with no evidence of the autoimmunity characteristic of type 1 diabetes”. Type 2 diabetes patients can still produce insulin, but the body is unable to use it. This information about insulin can be useful later if we want to perform feature selection, in order to boost this model’s performance", + "crumbs": [ + "End-to-end tutorial - Classification" + ] + }, + { + "objectID": "end_to_end_example.html#partial-dependency-plot", + "href": "end_to_end_example.html#partial-dependency-plot", + "title": "End-to-end tutorial - Classification", + "section": "Partial Dependency Plot", + "text": "Partial Dependency Plot\n\nnum_features = X_train_processed.columns.tolist()\nnum_features\n\n['Pregnancies',\n 'Glucose',\n 'BloodPressure',\n 'SkinThickness',\n 'Insulin',\n 'BMI',\n 'DiabetesPedigreeFunction',\n 'Age']\n\n\n\npdp_numerical_only(final_model,X_train_processed,num_features,class_names=['no diabetes','diabetes'],\n nrows=4,ncols=2)\n# y_class is ignored in binary classification or classical regression settings.\n\n\n\n\n\n\n\n\nFor Partial Dependency Plot, Glucose, BMI and Age show the most changes compared to other features. This matches the feature importances plot earlier.\n\nplot_ice_pair(final_model,X_train_processed,pair_features=['Glucose','Age'],class_idx=1,figsize=(10,4))\n# class_idx is ignored in binary classification or classical regression settings.", + "crumbs": [ + "End-to-end tutorial - Classification" + ] + }, + { + "objectID": "ml_helpers.html", + "href": "ml_helpers.html", + "title": "ml_helpers", + "section": "", + "text": "source\n\nrun_logistic_regression\n\n run_logistic_regression (X_trn:pandas.core.frame.DataFrame,\n y_trn:Union[pandas.core.series.Series,numpy.ndar\n ray], multi_class='multinomial', solver='newton-\n cg', penalty=None, max_iter=10000,\n return_coef=False)\n\nPerform Sklearn logistic regression, then print coefficients and classification report\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nX_trn\npd.DataFrame\n\nTraining dataframe\n\n\ny_trn\npd.Series | np.ndarray\n\nTraining label\n\n\nmulti_class\nstr\nmultinomial\nsklearn’s log reg multiclass option\n\n\nsolver\nstr\nnewton-cg\nsklearn’s log reg solver option\n\n\npenalty\nNoneType\nNone\nsklearn’s log reg penalty option\n\n\nmax_iter\nint\n10000\nsklearn’s log reg max iteration option\n\n\nreturn_coef\nbool\nFalse\nwhether to return coefficients\n\n\n\n\nsource\n\n\nrun_multinomial_statmodel\n\n run_multinomial_statmodel (X_trn:pandas.core.frame.DataFrame,\n y_trn:Union[pandas.core.series.Series,numpy.nd\n array], add_constant=True)\n\nPerform multinominal logit from statsmodel, then print results and classification report\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nX_trn\npd.DataFrame\n\nTraining dataframe\n\n\ny_trn\npd.Series | np.ndarray\n\nTraining label\n\n\nadd_constant\nbool\nTrue\nTo add a constant column to X_trn\n\n\n\n\nsource\n\n\nrun_sklearn_model\n\n run_sklearn_model (model_name:str, model_params:dict,\n X_trn:pandas.core.frame.DataFrame,\n y_trn:Union[pandas.core.series.Series,numpy.ndarray],\n is_regression=False, class_names:list=None,\n test_split=None, metric_funcs={}, seed=42,\n plot_fea_imp=True)\n\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nmodel_name\nstr\n\nsklearn’s Machine Learning model to try. Currently support DecisionTree,AdaBoost,RandomForest\n\n\nmodel_params\ndict\n\nA dictionary containing model’s hyperparameters\n\n\nX_trn\npd.DataFrame\n\nTraining dataframe\n\n\ny_trn\npd.Series | np.ndarray\n\nTraining label\n\n\nis_regression\nbool\nFalse\nTo use regression model or classification model\n\n\nclass_names\nlist\nNone\nList of names associated with the labels (same order); e.g. [‘no’,‘yes’]. For classification only\n\n\ntest_split\nNoneType\nNone\nTest set split. If float: random split. If list of list: indices of train and test set. If None: skip splitting\n\n\nmetric_funcs\ndict\n{}\nDictionary of metric functions: {metric_name:metric_func}\n\n\nseed\nint\n42\nRandom seed\n\n\nplot_fea_imp\nbool\nTrue\nTo whether plot sklearn’s feature importances. Set to False to skip the plot\n\n\n\n\nsource\n\n\ntune_sklearn_model\n\n tune_sklearn_model (model_name:str, param_grid:dict,\n X_trn:pandas.core.frame.DataFrame,\n y_trn:Union[pandas.core.series.Series,numpy.ndarray],\n is_regression=False, custom_cv=5,\n random_cv_iter=None, scoring=None, seed=42,\n rank_show=10, show_split_scores=True)\n\nPerform either Sklearn’s Grid Search or Randomized Search (based on random_cv_iter) of the model using param_grid\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nmodel_name\nstr\n\nsklearn’s Machine Learning model to try. Currently support DecisionTree,AdaBoost,RandomForest,\n\n\nparam_grid\ndict\n\nDictionary with parameters names (str) as keys and lists of parameter settings to try as values\n\n\nX_trn\npd.DataFrame\n\nTraining dataframe\n\n\ny_trn\npd.Series | np.ndarray\n\nTraining label\n\n\nis_regression\nbool\nFalse\nIs it a regression problem, or classification?\n\n\ncustom_cv\nint\n5\nsklearn’s cross-validation splitting strategy\n\n\nrandom_cv_iter\nNoneType\nNone\nNumber of parameter settings that are sampled. Use this if you want to do RandomizedSearchCV\n\n\nscoring\nNoneType\nNone\nMetric\n\n\nseed\nint\n42\nRandom seed\n\n\nrank_show\nint\n10\nNumber of ranks to show (descending order)\n\n\nshow_split_scores\nbool\nTrue\nTo show both train and test split scores\n\n\n\n\nsource\n\n\nget_adaboost_info\n\n get_adaboost_info (dt_params, ada_params, X, y, seed=42)\n\n\nsource\n\n\nshow_both_cv\n\n show_both_cv (search_cv, default_cv, scoring, top_n=10,\n show_split_scores=False)\n\n\nsource\n\n\nsummarize_default_cv\n\n summarize_default_cv (default_cv, s)\n\n\nsource\n\n\nsummarize_cv_results\n\n summarize_cv_results (search_cv, scoring, top_n=10,\n show_split_scores=False)\n\n\nsource\n\n\ndo_param_search\n\n do_param_search (X_train, y_train, estimator, param_grid,\n random_cv_iter=None, include_default=True, cv=None,\n scoring=None, seed=42)", + "crumbs": [ + "ml_helpers" + ] + } +] \ No newline at end of file diff --git a/site_libs/bootstrap/bootstrap-icons.css 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0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow{bottom:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow{left:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-end .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow::before{right:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-right-color:var(--bs-tooltip-bg)}.bs-tooltip-bottom .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow{top:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-bottom .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow::before{bottom:-1px;border-width:0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-start .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow{right:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-start .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) 0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:0.875rem;--bs-popover-bg: #fff;--bs-popover-border-width: 1px;--bs-popover-border-color: rgba(0, 0, 0, 0.175);--bs-popover-border-radius: 0.5rem;--bs-popover-inner-border-radius: calc(0.5rem - 1px);--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-popover-header-padding-x: 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#fff);--bslib-value-box-border-color-default: var(--bs-card-border-color, rgba(0, 0, 0, 0.175));color:var(--bslib-value-box-color);background-color:var(--bslib-value-box-bg, var(--bslib-value-box-bg-default));border-color:var(--bslib-value-box-border-color, var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box .value-box-showcase,.bslib-value-box .value-box-showcase>.html-fill-item{width:100%}.bslib-value-box[data-full-screen=true] .value-box-showcase{max-height:var(---bslib-value-box-showcase-max-h-fs, 100%)}@media screen and (min-width: 575.98px){@container bslib-value-box (max-width: 300px){.bslib-value-box:not(.showcase-bottom) .value-box-grid{grid-template-columns:1fr !important;grid-template-rows:auto auto;grid-template-areas:"top" 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t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&N.on(this._element,ft,(t=>this._keydown(t))),"hover"===this._config.pause&&(N.on(this._element,pt,(()=>this.pause())),N.on(this._element,mt,(()=>this._maybeEnableCycle()))),this._config.touch&&st.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of z.find(".carousel-item img",this._element))N.on(t,gt,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(ct)),rightCallback:()=>this._slide(this._directionToOrder(ht)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new st(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=Tt[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=z.findOne(wt,this._indicatorsElement);e.classList.remove(yt),e.removeAttribute("aria-current");const i=z.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(yt),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===at,s=e||b(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>N.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(dt).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),d(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(yt),i.classList.remove(yt,c,l),this._isSliding=!1,r(ut)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return z.findOne(Et,this._element)}_getItems(){return z.find(At,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return p()?t===ct?lt:at:t===ct?at:lt}_orderToDirection(t){return p()?t===lt?ct:ht:t===lt?ht:ct}static jQueryInterface(t){return this.each((function(){const e=xt.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}N.on(document,bt,"[data-bs-slide], 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i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt,Nt),this._element.style[e]="",N.trigger(this._element,St)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(N.trigger(this._element,Dt).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,d(this._element),this._element.classList.add(Mt),this._element.classList.remove(Pt,Nt);for(const t of this._triggerArray){const e=z.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt),N.trigger(this._element,$t)}),this._element,!0)}_isShown(t=this._element){return 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J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"

"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(us);const fs=".bs.scrollspy",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s="active",bs="[href]",vs=".nav-link",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},As={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Es extends W{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const 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t=e.state,n=e.name,r=t.rects.reference,o=t.rects.popper,i=t.modifiersData.preventOverflow,a=J(t,{elementContext:"reference"}),s=J(t,{altBoundary:!0}),f=ve(a,r),c=ve(s,o,i),p=ye(f),u=ye(c);t.modifiersData[n]={referenceClippingOffsets:f,popperEscapeOffsets:c,isReferenceHidden:p,hasPopperEscaped:u},t.attributes.popper=Object.assign({},t.attributes.popper,{"data-popper-reference-hidden":p,"data-popper-escaped":u})}},be=Z({defaultModifiers:[ee,te,oe,ie]}),xe=[ee,te,oe,ie,ae,le,he,me,ge],we=Z({defaultModifiers:xe});e.applyStyles=ie,e.arrow=me,e.computeStyles=oe,e.createPopper=we,e.createPopperLite=be,e.defaultModifiers=xe,e.detectOverflow=J,e.eventListeners=ee,e.flip=le,e.hide=ge,e.offset=ae,e.popperGenerator=Z,e.popperOffsets=te,e.preventOverflow=he,Object.defineProperty(e,"__esModule",{value:!0})})); + diff --git a/site_libs/quarto-html/quarto-syntax-highlighting.css b/site_libs/quarto-html/quarto-syntax-highlighting.css new file mode 100644 index 0000000..d9fd98f --- /dev/null +++ b/site_libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/site_libs/quarto-html/tippy.css b/site_libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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b/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,288 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver(() => { + setTimeout(updateDocumentOffsetWithoutAnimation, 0); + }); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/site_libs/quarto-search/autocomplete.umd.js b/site_libs/quarto-search/autocomplete.umd.js new file mode 100644 index 0000000..ae0063a --- /dev/null +++ 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0===r?I.includeMatches:r,o=n.includeScore,c=void 0===o?I.includeScore:o,a=[];return i&&a.push(de),c&&a.push(ve),e.map((function(e){var n=e.idx,r={item:t[n],refIndex:n};return a.length&&a.forEach((function(t){t(e,r)})),r}))}var ye=function(){function e(n){var i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},o=arguments.length>2?arguments[2]:void 0;r(this,e),this.options=t(t({},I),i),this.options.useExtendedSearch,this._keyStore=new S(this.options.keys),this.setCollection(n,o)}return o(e,[{key:"setCollection",value:function(e,t){if(this._docs=e,t&&!(t instanceof $))throw new Error("Incorrect 'index' type");this._myIndex=t||F(this.options.keys,this._docs,{getFn:this.options.getFn,fieldNormWeight:this.options.fieldNormWeight})}},{key:"add",value:function(e){k(e)&&(this._docs.push(e),this._myIndex.add(e))}},{key:"remove",value:function(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:function(){return!1},t=[],n=0,r=this._docs.length;n1&&void 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s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t(); \ No newline at end of file diff --git a/site_libs/quarto-search/quarto-search.js b/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..5f723d7 --- /dev/null +++ b/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1286 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + placeholder: language["search-text-placeholder"], + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // If this is a file URL, note that + + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + document.addEventListener("keyup", (event) => { + const { key } = event; + const kbds = quartoSearchOptions["keyboard-shortcut"]; + const focusedEl = document.activeElement; + + const isFormElFocused = [ + "input", + "select", + "textarea", + "button", + "option", + ].find((tag) => { + return focusedEl.tagName.toLowerCase() === tag; + }); + + if ( + kbds && + kbds.includes(key) && + !isFormElFocused && + !document.activeElement.isContentEditable + ) { + event.preventDefault(); + window.quartoOpenSearch(); + } + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = throttle(() => { + // Only do this if we're not detached + // Bug #7117 + // This will happen when the keyboard is shown on ios (resulting in a scroll) + // which then closed the search UI + if (!window.matchMedia(detachedMediaQuery).matches) { + setIsOpen(false); + } + }, 50); + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.flatMap((event) => { + // This API limits the number of items per event to 20 + const chunkSize = 20; + const itemChunks = []; + const eventItems = event.items; + for (let i = 0; i < eventItems.length; i += chunkSize) { + itemChunks.push(eventItems.slice(i, i + chunkSize)); + } + // Split the items into multiple events that can be sent + const events = itemChunks.map((items) => { + return { + ...event, + items, + }; + }); + return events; + }); + + for (const event of events) { + insights.viewedObjectIDs(event); + } + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +var shownWarning = false; + +// fuse index options +const kFuseIndexOptions = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, +}; + +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + if (window.location.protocol === "file:" && !shownWarning) { + window.alert( + "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." + ); + shownWarning = true; + return; + } + const fuse = new window.Fuse([], kFuseIndexOptions); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href, + item.crumbs, + quartoSearchOptions + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard( + createElement, + icon, + title, + section, + text, + href, + crumbs, + quartoSearchOptions +) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContents = [iconEl, titleEl]; + const showParent = quartoSearchOptions["show-item-context"]; + if (crumbs && showParent) { + let crumbsOut = undefined; + const crumbClz = ["search-result-crumbs"]; + if (showParent === "root") { + crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; + } else if (showParent === "parent") { + crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; + } else { + crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; + crumbClz.push("search-result-crumbs-wrap"); + } + + const crumbEl = createElement( + "p", + { class: crumbClz.join(" ") }, + crumbsOut + ); + titleContents.push(crumbEl); + } + + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + titleContents + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text", "crumbs"].forEach( + (keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + } + ); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +let subSearchTerm = undefined; +let subSearchFuse = undefined; +const kFuseMaxWait = 125; + +async function fuseSearch(query, fuse, fuseOptions) { + let index = fuse; + // Fuse.js using the Bitap algorithm for text matching which runs in + // O(nm) time (no matter the structure of the text). In our case this + // means that long search terms mixed with large index gets very slow + // + // This injects a subIndex that will be used once the terms get long enough + // Usually making this subindex is cheap since there will typically be + // a subset of results matching the existing query + if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { + // Use the existing subSearchFuse + index = subSearchFuse; + } else if (subSearchFuse !== undefined) { + // The term changed, discard the existing fuse + subSearchFuse = undefined; + subSearchTerm = undefined; + } + + // Search using the active fuse + const then = performance.now(); + const resultsRaw = await index.search(query, fuseOptions); + const now = performance.now(); + + const results = resultsRaw.map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + crumbs: result.item.crumbs, + }; + }); + + // If we don't have a subfuse and the query is long enough, go ahead + // and create a subfuse to use for subsequent queries + if (now - then > kFuseMaxWait && subSearchFuse === undefined) { + subSearchTerm = query; + subSearchFuse = new window.Fuse([], kFuseIndexOptions); + resultsRaw.forEach((rr) => { + subSearchFuse.add(rr.item); + }); + } + return results; +} diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 0000000..d80a786 --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,27 @@ + + + + https://anhquan0412.github.io/that-ml-library/chart_plotting.html + 2024-02-19T03:50:35.188Z + + + https://anhquan0412.github.io/that-ml-library/index.html + 2024-02-19T03:50:34.944Z + + + https://anhquan0412.github.io/that-ml-library/data_preprocess.html + 2024-02-19T03:50:36.604Z + + + https://anhquan0412.github.io/that-ml-library/utils.html + 2024-02-19T03:50:34.968Z + + + https://anhquan0412.github.io/that-ml-library/end_to_end_example.html + 2024-02-19T03:50:35.040Z + + + https://anhquan0412.github.io/that-ml-library/ml_helpers.html + 2024-02-19T03:50:36.876Z + + diff --git a/styles.css b/styles.css new file mode 100644 index 0000000..66ccc49 --- /dev/null +++ b/styles.css @@ -0,0 +1,37 @@ +.cell { + margin-bottom: 1rem; +} + +.cell > .sourceCode { + margin-bottom: 0; +} + +.cell-output > pre { + margin-bottom: 0; +} + +.cell-output > pre, .cell-output > .sourceCode > pre, .cell-output-stdout > pre { + margin-left: 0.8rem; + margin-top: 0; + background: none; + border-left: 2px solid lightsalmon; + border-top-left-radius: 0; + border-top-right-radius: 0; +} + +.cell-output > .sourceCode { + border: none; +} + +.cell-output > .sourceCode { + background: none; + margin-top: 0; +} + +div.description { + padding-left: 2px; + padding-top: 5px; + font-style: italic; + font-size: 135%; + opacity: 70%; +} diff --git a/utils.html b/utils.html new file mode 100644 index 0000000..97b4a7b --- /dev/null +++ b/utils.html @@ -0,0 +1,664 @@ + + + + + + + + + + +that-ml-library - utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + +
+ + + +
+ +
+
+

utils

+
+ +
+
+ This module contains some utility functions +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

val2list

+
+
 val2list (val, lsize=1)
+
+

Convert an element (nonlist value) to a list of 1 element

+
+
val2list(2)
+
+
[2]
+
+
+
+
val2list(2,lsize=5)
+
+
[2, 2, 2, 2, 2]
+
+
+
+
val2list([2,3])
+
+
[2, 3]
+
+
+
+
val2list(np.array([1,2,3]))
+
+
array([1, 2, 3])
+
+
+
+

source

+
+
+

create_dir

+
+
 create_dir (path_dir)
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file
+ + +