matti_jms_collabs/citizen_shield/citizen_shield_demographics_classification.ipynb

Classifying demographics data using mask based detertminants in the citizen shield project¶

This script relies on the main following functions:¶

• plot_decision_boundary()
• plot_learning_curve()
• multi_roc_auc_plot()
• optimize_model()
• plot_confusion_matrix()
• summary_performance_metrics_classification()
• CatBoostClassifier()
• shap.TreeExplainer()

In [1]:

import os
tmp = os.getcwd()
os.chdir(tmp.split("matti_jms_collabs")[0] + "jmspack")

In [2]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import shap
from jmspack.ml_utils import (
                              plot_decision_boundary,
                              plot_cv_indices,
                              plot_learning_curve,
                              dict_of_models,
                              multi_roc_auc_plot,
                              optimize_model,
                              plot_confusion_matrix,
                              summary_performance_metrics_classification
                          )

from jmspack.utils import JmsColors
from sklearn.model_selection import (
    TimeSeriesSplit,
    KFold,
    ShuffleSplit,
    StratifiedKFold,
    GroupShuffleSplit,
    GroupKFold,
    StratifiedShuffleSplit
)

from sklearn.model_selection import (
    GridSearchCV, 
                                     RepeatedStratifiedKFold, 
#                                      cross_val_score, 
                                     cross_validate, 
#                                      train_test_split, 
#                                      KFold
)

In [3]:

# list of potential sklearn models
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier

In [4]:

from sklearn.metrics import confusion_matrix

In [5]:

from catboost import CatBoostClassifier

In [6]:

shap.initjs()

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In [7]:

os.chdir(tmp)

In [8]:

if "jms_style_sheet" in plt.style.available:
    _ = plt.style.use("jms_style_sheet")

In [9]:

df = (pd.read_csv("kp_determinants/kp_determinants_and_demographics.csv")
#       .assign(**{"demographic_age_binary": lambda d: (d["demographic_age"] > 8).astype(int)})
     )

In [10]:

# df["demographic_age_binned"] = np.nan
# df.loc[df["demographic_age"] < 7, "demographic_age_binned"] = 0
# df.loc[df["demographic_age"].between(7, 10), "demographic_age_binned"] = 1
# df.loc[df["demographic_age"] > 10, "demographic_age_binned"] = 2

In [11]:

df["demographic_age_binned"] = np.nan
df.loc[df["demographic_age"] < 8, "demographic_age_binned"] = 0
df.loc[df["demographic_age"] > 9, "demographic_age_binned"] = 1

In [12]:

df["demographic_gender_binned"] = np.nan
df.loc[df["demographic_gender"] == 1, "demographic_gender_binned"] = 0
df.loc[df["demographic_gender"] == 2, "demographic_gender_binned"] = 1

In [13]:

df["demographic_region_binned"] = np.nan
df.loc[df["demographic_region"] == 17, "demographic_region_binned"] = 0
df.loc[df["demographic_region"] != 17, "demographic_region_binned"] = 1

In [14]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1154 entries, 0 to 1153
Data columns (total 34 columns):
 #   Column                              Non-Null Count  Dtype  
---  ------                              --------------  -----  
 0   demographic_gender                  1154 non-null   int64  
 1   demographic_age                     1154 non-null   int64  
 2   demographic_region                  1148 non-null   float64
 3   demographic_education               1152 non-null   float64
 4   demographic_living_with             1137 non-null   float64
 5   demographic_underage_children       1131 non-null   object 
 6   demographic_income                  1150 non-null   float64
 7   effectiveness_masks                 1154 non-null   int64  
 8   effectiveness_hometests             1154 non-null   int64  
 9   effectiveness_ventilation           1154 non-null   int64  
 10  effectiveness_quarantine            1154 non-null   int64  
 11  effectiveness_avoid_meeting         1154 non-null   int64  
 12  effectiveness_announce_test_result  1154 non-null   int64  
 13  effectiveness_avoid_public_events   1154 non-null   int64  
 14  effectiveness_covid_passport        1154 non-null   int64  
 15  ease_masks                          1154 non-null   int64  
 16  ease_hometests                      1154 non-null   int64  
 17  ease_ventilation                    1154 non-null   int64  
 18  ease_quarantine                     1154 non-null   int64  
 19  ease_avoid_meeting                  1154 non-null   int64  
 20  ease_announce_test_result           1154 non-null   int64  
 21  ease_avoid_public_events            1154 non-null   int64  
 22  ease_covid_passport                 1154 non-null   int64  
 23  persistence_masks                   1154 non-null   int64  
 24  persistence_hometests               1154 non-null   int64  
 25  persistence_ventilation             1154 non-null   int64  
 26  persistence_quarantine              1154 non-null   int64  
 27  persistence_avoid_meeting           1154 non-null   int64  
 28  persistence_announce_test_result    1154 non-null   int64  
 29  persistence_avoid_public_events     1154 non-null   int64  
 30  persistence_covid_passport          1154 non-null   int64  
 31  demographic_age_binned              894 non-null    float64
 32  demographic_gender_binned           1150 non-null   float64
 33  demographic_region_binned           1154 non-null   float64
dtypes: float64(7), int64(26), object(1)
memory usage: 306.7+ KB

In [15]:

df = (df
      .dropna(axis=0, inplace=False)
      .assign(**{"demographic_age_binned": lambda d: d["demographic_age_binned"].astype(int)})
     )

In [16]:

display(df.head()); display(df.shape)

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	demographic_gender	demographic_age	demographic_region	demographic_education	demographic_living_with	demographic_underage_children	demographic_income	effectiveness_masks	effectiveness_hometests	effectiveness_ventilation	...	persistence_hometests	persistence_ventilation	persistence_quarantine	persistence_avoid_meeting	persistence_announce_test_result	persistence_avoid_public_events	persistence_covid_passport	demographic_age_binned	demographic_gender_binned	demographic_region_binned
0	1	7	17.0	2.0	2.0	Yes	3.0	2	1	5	...	4	5	4	4	5	5	0	0	0.0	0.0
1	1	4	17.0	3.0	2.0	Yes	2.0	3	1	5	...	1	5	1	1	5	4	5	0	0.0	0.0
2	1	6	17.0	4.0	2.0	Yes	3.0	5	5	5	...	4	5	3	3	5	1	4	0	0.0	0.0
3	1	7	17.0	4.0	2.0	Yes	3.0	5	5	4	...	5	5	5	3	5	3	5	0	0.0	0.0
4	1	4	17.0	2.0	1.0	No	4.0	1	1	3	...	1	1	1	1	1	1	1	0	0.0	0.0

5 rows × 34 columns

(859, 34)

Plot the amount of rows in each side of the target¶

In [17]:

use_binned_target = True

In [18]:

unbinned_target = "demographic_region"

In [19]:

_ = sns.countplot(x=df[unbinned_target])
print("Amount in each outcome")
df[unbinned_target].value_counts()

Amount in each outcome

Out[19]:

17.0    277
10.0     84
18.0     75
13.0     54
14.0     45
16.0     41
7.0      38
15.0     36
11.0     32
12.0     31
5.0      27
2.0      24
9.0      24
3.0      20
8.0      17
6.0      12
1.0      12
4.0      10
Name: demographic_region, dtype: int64

In [20]:

binned_target = f"{unbinned_target}_binned"

In [21]:

_ = sns.countplot(x=df[binned_target])
print("Amount in each outcome")
df[binned_target].value_counts()

Amount in each outcome

Out[21]:

1.0    582
0.0    277
Name: demographic_region_binned, dtype: int64

In [22]:

if use_binned_target:
    target = binned_target
else:
    target = unbinned_target

In [23]:

# target = "demographic_age"

dict_of_models¶

In [24]:

# dict_of_models

In [25]:

new_models_dict = [
     {'label': 'Logistic Regression', 'model': LogisticRegression()},
     {'label': 'Gradient Boosting', 'model': GradientBoostingClassifier()},
#      {'label': 'K_Neighbors Classifier',
#       'model': KNeighborsClassifier(n_neighbors=3)},
#      {'label': 'SVM Classifier (linear)',
#       'model': SVC(C=0.025, kernel='linear', probability=True)},
#      {'label': 'SVM Classifier (Radial Basis Function; RBF)',
#       'model': SVC(C=1, gamma=2, probability=True)},
     {'label': 'Gaussian Process Classifier',
      'model': GaussianProcessClassifier(kernel=1**2 * RBF(length_scale=1))},
     {'label': 'Decision Tree (depth=5)',
      'model': DecisionTreeClassifier(max_depth=5)},
     {'label': 'Random Forest Classifier(depth=5)',
      'model': RandomForestClassifier(max_depth=5, max_features=1, n_estimators=10)},
     {'label': 'Multilayer Perceptron (MLP) Classifier',
      'model': MLPClassifier(alpha=1, max_iter=1000)},
     {'label': 'AdaBoost Classifier', 'model': AdaBoostClassifier()},
     {'label': 'Naive Bayes (Gaussian) Classifier', 'model': GaussianNB()},
     {'label': 'Quadratic Discriminant Analysis Classifier',
      'model': QuadraticDiscriminantAnalysis()},
    {'label': 'Categorical Boosted Classifier(depth=5)',
      'model': CatBoostClassifier(iterations=500,
                               depth=5,
                               learning_rate=None,
                               loss_function="Logloss",
                               verbose=False,
                                  random_seed=69420,
                                 allow_writing_files=False)}
]

multi_roc_auc_plot¶

In [26]:

feature_list = df.drop(df.filter(regex="demographic").columns.tolist(), axis=1).columns.tolist()
X = df.loc[:, feature_list]
y = df.loc[:, target]

In [27]:

if y.nunique() == 2:
    # fig, ax = multi_roc_auc_plot(X=X, y=y)
    fig, ax = multi_roc_auc_plot(X=X, y=y, models=new_models_dict)

In [28]:

# AdaBoostClassifier(random_state=69420).get_params().keys()

In [29]:

(optimized_estimator, feature_ranking, feature_selected, feature_importance, optimal_params) = \
optimize_model(X=X, 
               y=y,
               estimator=RandomForestClassifier(random_state=69420),
               grid_params_dict = {'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 
                                   'n_estimators': [1, 2, 3, 4, 5, 10, 20, 30, 40, 50], 
                                   'max_features': ['log2', 'auto', 'sqrt'], 
                                   'criterion': ['gini', 'entropy']},
               gridsearch_kwargs = {'scoring': 'accuracy', 
                                    'cv': 5, 
                                    'n_jobs': -2},
               rfe_kwargs = {'n_features_to_select': 4, 'verbose': 1}
              )

Fitting estimator with 24 features.
Fitting estimator with 23 features.
Fitting estimator with 22 features.
Fitting estimator with 21 features.
Fitting estimator with 20 features.
Fitting estimator with 19 features.
Fitting estimator with 18 features.
Fitting estimator with 17 features.
Fitting estimator with 16 features.
Fitting estimator with 15 features.
Fitting estimator with 14 features.
Fitting estimator with 13 features.
Fitting estimator with 12 features.
Fitting estimator with 11 features.
Fitting estimator with 10 features.
Fitting estimator with 9 features.
Fitting estimator with 8 features.
Fitting estimator with 7 features.
Fitting estimator with 6 features.
Fitting estimator with 5 features.

- Sizes :
- X shape = (859, 24)
- y shape = (859,)
- X_train shape = (601, 24)
- X_test shape = (258, 24)
- y_train shape = (601,)
- y_test shape = (258,)

- Model info :
- Optimal Parameters = {'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 6, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 30, 'n_jobs': None, 'oob_score': False, 'random_state': 69420, 'verbose': 0, 'warm_start': False}
- Selected feature list = ['effectiveness_masks', 'effectiveness_hometests', 'effectiveness_ventilation', 'ease_ventilation']
- Accuracy score on test set = 60.9%

In [30]:

feature_importance_df = (feature_importance
                         .to_frame(name=optimal_params["criterion"].iloc[0]).sort_values(by=optimal_params["criterion"].iloc[0], ascending=False)
                         .reset_index()
                         .rename(columns={"index": "feature"})
                        )
feature_importance_df.head()

Out[30]:

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	feature	gini
0	ease_ventilation	0.08
1	persistence_avoid_public_events	0.06
2	effectiveness_avoid_public_events	0.06
3	effectiveness_masks	0.05
4	ease_announce_test_result	0.05

In [31]:

_ = plt.figure(figsize=(7, 4))
_ = sns.barplot(data=feature_importance_df, x=optimal_params["criterion"].iloc[0], y="feature")
# _ = plt.savefig("images/feature_importances_citizen_shield_age_all.png", dpi=400, bbox_inches="tight")

Shap beeswarm plot with the optimized model¶

In [32]:

explainer = shap.TreeExplainer(optimized_estimator, X)
shap_values = explainer(X)

In [33]:

shap.plots.beeswarm(shap_values[:, :, 1], max_display=25,)

Reset X with the newly selected features¶

In [34]:

feature_list = feature_selected
X = df[feature_list]
y = df[target]

multi_roc_auc_plot¶

Run all models with the selected features out of curiosity¶

In [35]:

if y.nunique() == 2:
    # fig, ax = multi_roc_auc_plot(X=X, y=y)
    fig, ax = multi_roc_auc_plot(X=X, y=y, models=new_models_dict, figsize=(5,5))

In [36]:

if y.nunique() == 2:
    fig, ax = multi_roc_auc_plot(X=X, 
                                 y=y, 
                                 models=[{"label": "optimized random forest", "model": optimized_estimator},
                                         {'label': 'AdaBoost Classifier', 'model': AdaBoostClassifier()},
                                         {'label': 'Gaussian Process Classifier', 'model': GaussianProcessClassifier(kernel=1**2 * RBF(length_scale=1))},
                                         {'label': 'Categorical Boosted Classifier(depth=5)',  'model': CatBoostClassifier(iterations=500,
                                                                                                                           depth=5,
                                                                                                                           learning_rate=None,
                                                                                                                           loss_function="Logloss",
                                                                                                                           verbose=False,
                                                                                                                           random_seed=69420,
                                                                                                                          allow_writing_files=False)}
                                        ],
                                figsize=(6,6))

In [37]:

# optimized_estimator = new_models_dict[6]['model']

plot_learning_curve¶

In [38]:

fig, ax = plot_learning_curve(X=X, 
                              y=y, 
                              estimator=optimized_estimator, 
                              title = f'Learning Curve {optimized_estimator.__class__.__name__}',
                             scoring = 'accuracy')

plot_decision_boundary¶

In [39]:

if X.shape[1] == 2:
    fig, ax = plot_decision_boundary(X=X, 
                                     y=y, 
                                     clf=optimized_estimator, 
                                     title=f"Decision Boundary {optimized_estimator.__class__.__name__}",
                                     legend_title = target,
                                     h=0.1,
                                     figsize = (6, 4)
                                    )

Plot the first two columns against each other¶

In [40]:

scatter_df = pd.concat([X, y], axis=1)

scatter_df[X.columns.tolist()[0]] = scatter_df[X.columns.tolist()[0]] + pd.Series(np.random.normal(loc=0.0, scale=0.1, size=scatter_df.shape[0]))
scatter_df[X.columns.tolist()[1]] = scatter_df[X.columns.tolist()[1]] + pd.Series(np.random.normal(loc=0.0, scale=0.1, size=scatter_df.shape[0]))

scatter_df[target]=scatter_df[target].astype("category")

In [41]:

_ = sns.scatterplot(data=scatter_df.dropna(), 
                   x=X.columns.tolist()[0],
                   y=X.columns.tolist()[1],
                   hue=target,
                   alpha=0.45)

plot_confusion_matrix¶

In [42]:

clf = optimized_estimator

train_set_percentage = 0.6
train_X = X.sample(frac=train_set_percentage, random_state=69420)
train_y = y.loc[train_X.index]
test_X = X.drop(train_X.index, axis=0)
test_y = y.drop(train_X.index, axis=0)

In [43]:

_ = clf.fit(X = train_X, 
            y = train_y)
    
pred_y = clf.predict(test_X)
cf_matrix = confusion_matrix(test_y, pred_y)

fig, ax = plot_confusion_matrix(cf=cf_matrix, title=f"Confusion Matrix classifying {target}")

summary_performance_metrics_classification¶

In [44]:

if y.nunique() == 2:
    metrics_df = summary_performance_metrics_classification(model=clf, X_test=test_X, y_true=test_y)
    display(metrics_df)

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	TN	FP	FN	TP	Accuracy	Balanced Accuracy	Prevalence	Sensitivity	Specificity	PPV	NPV	auc	Mean AUC (CI 5%-95%)	F1
scores	17	80	32	215	0.674	0.523	0.718	0.87	0.175	0.729	0.347	0.564	0.562 (95% CI 0.562-0.562)	0.793

plotting cross validations of the optimized model with selected features¶

In [45]:

cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=10, random_state=1)

In [46]:

# import sklearn
# sorted(sklearn.metrics.SCORERS.keys())

In [47]:

if y.nunique() == 2:
    scoring_list = ('accuracy',
     'balanced_accuracy',
     'f1',
     'f1_weighted',
     'precision',
     'precision_weighted',
     'recall',
     'recall_weighted',
     'roc_auc',
                   )
else:
    scoring_list = ('accuracy',
                    'balanced_accuracy',
#                    "explained_variance", 
                    "normalized_mutual_info_score",
                   'v_measure_score'
                   )

In [48]:

tmp_out = cross_validate(optimized_estimator, 
                         X, 
                         y, 
                         scoring=scoring_list, 
                         return_train_score=False, 
                         cv=cv, 
                         n_jobs=-1,
#                         fit_params={"sample_weight": sampling_weights} # fit_params is returning nans for some reason :/
                        )

In [49]:

pd.DataFrame(tmp_out).drop(["fit_time", "score_time"], axis=1).agg(["mean", "median", "std"]).round(3)

Out[49]:

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	test_accuracy	test_balanced_accuracy	test_f1	test_f1_weighted	test_precision	test_precision_weighted	test_recall	test_recall_weighted	test_roc_auc
mean	0.661	0.496	0.793	0.555	0.676	0.543	0.960	0.661	0.542
median	0.663	0.493	0.794	0.557	0.675	0.534	0.966	0.663	0.545
std	0.019	0.019	0.013	0.022	0.009	0.095	0.026	0.019	0.062

In [50]:

cv_metrics_df = pd.DataFrame(tmp_out).drop(["fit_time", "score_time"], axis=1).melt(var_name="Metric")

In [51]:

_ = plt.figure(figsize=(25,5))
_ = sns.boxplot(data = cv_metrics_df,
                x = "Metric",
                y = "value")
_ = sns.swarmplot(data = cv_metrics_df,
                x = "Metric",
                y = "value", edgecolor="white", linewidth=1)
_ = plt.title(f"All performance metrics {optimized_estimator.__class__.__name__} with cross validation")

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