From 218b6845149b5cbbffa3551cea1dd2d0018d53a5 Mon Sep 17 00:00:00 2001 From: Primoz Date: Thu, 24 Nov 2022 16:12:20 +0100 Subject: [PATCH] Automize clustering classification logic and add parameters at the begining of the scripts. General changes and improvements. --- exploration/ml_pipeline_classification.py | 143 +++++++++--------- ...pipeline_classification_with_clustering.py | 134 +++++++++++----- 2 files changed, 164 insertions(+), 113 deletions(-) diff --git a/exploration/ml_pipeline_classification.py b/exploration/ml_pipeline_classification.py index fc2fb81..ad460d3 100644 --- a/exploration/ml_pipeline_classification.py +++ b/exploration/ml_pipeline_classification.py @@ -46,7 +46,9 @@ import machine_learning.model # # RAPIDS models # %% [markdown] -# ## PANAS negative affect +# ## Set script's parameters +cv_method_str = 'logo' # logo, halflogo, 5kfold # Cross-validation method (could be regarded as a hyperparameter) +n_sl = 1 # Number of largest/smallest accuracies (of particular CV) outputs # %% jupyter={"source_hidden": true} model_input = pd.read_csv("../data/intradaily_30_min_all_targets/input_JCQ_job_demand_mean.csv") @@ -57,14 +59,13 @@ model_input.set_index(index_columns, inplace=True) # %% jupyter={"source_hidden": true} bins = [-10, -1, 1, 10] # bins for z-scored targets -model_input['target'], edges = pd.cut(model_input.target, bins=bins, labels=['low', 'medium', 'high'], retbins=True, right=False) #['low', 'medium', 'high'] +model_input['target'], edges = pd.cut(model_input.target, bins=bins, labels=['low', 'medium', 'high'], retbins=True, right=True) #['low', 'medium', 'high'] model_input['target'].value_counts(), edges model_input = model_input[model_input['target'] != "medium"] model_input['target'] = model_input['target'].astype(str).apply(lambda x: 0 if x == "low" else 1) model_input['target'].value_counts() -cv_method_str = 'logo' # logo, halflogo, 5kfold if cv_method_str == 'halflogo': model_input['pid_index'] = model_input.groupby('pid').cumcount() model_input['pid_count'] = model_input.groupby('pid')['pid'].transform('count') @@ -106,10 +107,6 @@ if cv_method_str == 'logo' or cv_method_str == 'half_logo': data_y, groups=data_groups, ) -# %% jupyter={"source_hidden": true} -# %% [markdown] -# ### Set n for nlargest and nsmallest -n = 5 # %% jupyter={"source_hidden": true} imputer = SimpleImputer(missing_values=np.nan, strategy='median') @@ -130,12 +127,12 @@ dummy_classifier = cross_validate( scoring=('accuracy', 'average_precision', 'recall', 'f1') ) # %% jupyter={"source_hidden": true} -print("Acc", np.median(dummy_classifier['test_accuracy'])) -print("Precision", np.median(dummy_classifier['test_average_precision'])) -print("Recall", np.median(dummy_classifier['test_recall'])) -print("F1", np.median(dummy_classifier['test_f1'])) -print("Largest 5 ACC:", np.sort(-np.partition(-dummy_classifier['test_accuracy'], n)[:n])[::-1]) -print("Smallest 5 ACC:", np.sort(np.partition(dummy_classifier['test_accuracy'], n)[:n])) +print("Acc", np.mean(dummy_classifier['test_accuracy'])) +print("Precision", np.mean(dummy_classifier['test_average_precision'])) +print("Recall", np.mean(dummy_classifier['test_recall'])) +print("F1", np.mean(dummy_classifier['test_f1'])) +print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-dummy_classifier['test_accuracy'], n_sl)[:n_sl])[::-1]) +print(f"Smallest {n_sl} ACC:", np.sort(np.partition(dummy_classifier['test_accuracy'], n_sl)[:n_sl])) # %% [markdown] # ### Logistic Regression @@ -154,12 +151,12 @@ log_reg_scores = cross_validate( scoring=('accuracy', 'precision', 'recall', 'f1') ) # %% jupyter={"source_hidden": true} -print("Acc", np.median(log_reg_scores['test_accuracy'])) -print("Precision", np.median(log_reg_scores['test_precision'])) -print("Recall", np.median(log_reg_scores['test_recall'])) -print("F1", np.median(log_reg_scores['test_f1'])) -print("Largest 5 ACC:", np.sort(-np.partition(-log_reg_scores['test_accuracy'], n)[:n])[::-1]) -print("Smallest 5 ACC:", np.sort(np.partition(log_reg_scores['test_accuracy'], n)[:n])) +print("Acc", np.mean(log_reg_scores['test_accuracy'])) +print("Precision", np.mean(log_reg_scores['test_precision'])) +print("Recall", np.mean(log_reg_scores['test_recall'])) +print("F1", np.mean(log_reg_scores['test_f1'])) +print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-log_reg_scores['test_accuracy'], n_sl)[:n_sl])[::-1]) +print(f"Smallest {n_sl} ACC:", np.sort(np.partition(log_reg_scores['test_accuracy'], n_sl)[:n_sl])) # %% [markdown] # ### Support Vector Machine @@ -178,12 +175,12 @@ svc_scores = cross_validate( scoring=('accuracy', 'precision', 'recall', 'f1') ) # %% jupyter={"source_hidden": true} -print("Acc", np.median(svc_scores['test_accuracy'])) -print("Precision", np.median(svc_scores['test_precision'])) -print("Recall", np.median(svc_scores['test_recall'])) -print("F1", np.median(svc_scores['test_f1'])) -print("Largest 5 ACC:", np.sort(-np.partition(-svc_scores['test_accuracy'], n)[:n])[::-1]) -print("Smallest 5 ACC:", np.sort(np.partition(svc_scores['test_accuracy'], n)[:n])) +print("Acc", np.mean(svc_scores['test_accuracy'])) +print("Precision", np.mean(svc_scores['test_precision'])) +print("Recall", np.mean(svc_scores['test_recall'])) +print("F1", np.mean(svc_scores['test_f1'])) +print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-svc_scores['test_accuracy'], n_sl)[:n_sl])[::-1]) +print(f"Smallest {n_sl} ACC:", np.sort(np.partition(svc_scores['test_accuracy'], n_sl)[:n_sl])) # %% [markdown] # ### Gaussian Naive Bayes @@ -203,12 +200,12 @@ gaussian_nb_scores = cross_validate( scoring=('accuracy', 'precision', 'recall', 'f1') ) # %% jupyter={"source_hidden": true} -print("Acc", np.median(gaussian_nb_scores['test_accuracy'])) -print("Precision", np.median(gaussian_nb_scores['test_precision'])) -print("Recall", np.median(gaussian_nb_scores['test_recall'])) -print("F1", np.median(gaussian_nb_scores['test_f1'])) -print("Largest 5 ACC:", np.sort(-np.partition(-gaussian_nb_scores['test_accuracy'], n)[:n])[::-1]) -print("Smallest 5 ACC:", np.sort(np.partition(gaussian_nb_scores['test_accuracy'], n)[:n])) +print("Acc", np.mean(gaussian_nb_scores['test_accuracy'])) +print("Precision", np.mean(gaussian_nb_scores['test_precision'])) +print("Recall", np.mean(gaussian_nb_scores['test_recall'])) +print("F1", np.mean(gaussian_nb_scores['test_f1'])) +print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-gaussian_nb_scores['test_accuracy'], n_sl)[:n_sl])[::-1]) +print(f"Smallest {n_sl} ACC:", np.sort(np.partition(gaussian_nb_scores['test_accuracy'], n_sl)[:n_sl])) # %% [markdown] # ### Stochastic Gradient Descent Classifier @@ -228,12 +225,12 @@ sgdc_scores = cross_validate( scoring=('accuracy', 'precision', 'recall', 'f1') ) # %% jupyter={"source_hidden": true} -print("Acc", np.median(sgdc_scores['test_accuracy'])) -print("Precision", np.median(sgdc_scores['test_precision'])) -print("Recall", np.median(sgdc_scores['test_recall'])) -print("F1", np.median(sgdc_scores['test_f1'])) -print("Largest 5 ACC:", np.sort(-np.partition(-sgdc_scores['test_accuracy'], n)[:n])[::-1]) -print("Smallest 5 ACC:", np.sort(np.partition(sgdc_scores['test_accuracy'], n)[:n])) +print("Acc", np.mean(sgdc_scores['test_accuracy'])) +print("Precision", np.mean(sgdc_scores['test_precision'])) +print("Recall", np.mean(sgdc_scores['test_recall'])) +print("F1", np.mean(sgdc_scores['test_f1'])) +print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-sgdc_scores['test_accuracy'], n_sl)[:n_sl])[::-1]) +print(f"Smallest {n_sl} ACC:", np.sort(np.partition(sgdc_scores['test_accuracy'], n_sl)[:n_sl])) # %% [markdown] # ### K-nearest neighbors @@ -253,12 +250,12 @@ knn_scores = cross_validate( scoring=('accuracy', 'precision', 'recall', 'f1') ) # %% jupyter={"source_hidden": true} -print("Acc", np.median(knn_scores['test_accuracy'])) -print("Precision", np.median(knn_scores['test_precision'])) -print("Recall", np.median(knn_scores['test_recall'])) -print("F1", np.median(knn_scores['test_f1'])) -print("Largest 5 ACC:", np.sort(-np.partition(-knn_scores['test_accuracy'], n)[:n])[::-1]) -print("Smallest 5 ACC:", np.sort(np.partition(knn_scores['test_accuracy'], n)[:n])) +print("Acc", np.mean(knn_scores['test_accuracy'])) +print("Precision", np.mean(knn_scores['test_precision'])) +print("Recall", np.mean(knn_scores['test_recall'])) +print("F1", np.mean(knn_scores['test_f1'])) +print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-knn_scores['test_accuracy'], n_sl)[:n_sl])[::-1]) +print(f"Smallest {n_sl} ACC:", np.sort(np.partition(knn_scores['test_accuracy'], n_sl)[:n_sl])) # %% [markdown] # ### Decision Tree @@ -278,12 +275,12 @@ dtree_scores = cross_validate( scoring=('accuracy', 'precision', 'recall', 'f1') ) # %% jupyter={"source_hidden": true} -print("Acc", np.median(dtree_scores['test_accuracy'])) -print("Precision", np.median(dtree_scores['test_precision'])) -print("Recall", np.median(dtree_scores['test_recall'])) -print("F1", np.median(dtree_scores['test_f1'])) -print("Largest 5 ACC:", np.sort(-np.partition(-dtree_scores['test_accuracy'], n)[:n])[::-1]) -print("Smallest 5 ACC:", np.sort(np.partition(dtree_scores['test_accuracy'], n)[:n])) +print("Acc", np.mean(dtree_scores['test_accuracy'])) +print("Precision", np.mean(dtree_scores['test_precision'])) +print("Recall", np.mean(dtree_scores['test_recall'])) +print("F1", np.mean(dtree_scores['test_f1'])) +print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-dtree_scores['test_accuracy'], n_sl)[:n_sl])[::-1]) +print(f"Smallest {n_sl} ACC:", np.sort(np.partition(dtree_scores['test_accuracy'], n_sl)[:n_sl])) # %% [markdown] # ### Random Forest Classifier @@ -303,12 +300,12 @@ rfc_scores = cross_validate( scoring=('accuracy', 'precision', 'recall', 'f1') ) # %% jupyter={"source_hidden": true} -print("Acc", np.median(rfc_scores['test_accuracy'])) -print("Precision", np.median(rfc_scores['test_precision'])) -print("Recall", np.median(rfc_scores['test_recall'])) -print("F1", np.median(rfc_scores['test_f1'])) -print("Largest 5 ACC:", np.sort(-np.partition(-rfc_scores['test_accuracy'], n)[:n])[::-1]) -print("Smallest 5 ACC:", np.sort(np.partition(rfc_scores['test_accuracy'], n)[:n])) +print("Acc", np.mean(rfc_scores['test_accuracy'])) +print("Precision", np.mean(rfc_scores['test_precision'])) +print("Recall", np.mean(rfc_scores['test_recall'])) +print("F1", np.mean(rfc_scores['test_f1'])) +print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-rfc_scores['test_accuracy'], n_sl)[:n_sl])[::-1]) +print(f"Smallest {n_sl} ACC:", np.sort(np.partition(rfc_scores['test_accuracy'], n_sl)[:n_sl])) # %% [markdown] # ### Gradient Boosting Classifier @@ -328,12 +325,12 @@ gbc_scores = cross_validate( scoring=('accuracy', 'precision', 'recall', 'f1') ) # %% jupyter={"source_hidden": true} -print("Acc", np.median(gbc_scores['test_accuracy'])) -print("Precision", np.median(gbc_scores['test_precision'])) -print("Recall", np.median(gbc_scores['test_recall'])) -print("F1", np.median(gbc_scores['test_f1'])) -print("Largest 5 ACC:", np.sort(-np.partition(-gbc_scores['test_accuracy'], n)[:n])[::-1]) -print("Smallest 5 ACC:", np.sort(np.partition(gbc_scores['test_accuracy'], n)[:n])) +print("Acc", np.mean(gbc_scores['test_accuracy'])) +print("Precision", np.mean(gbc_scores['test_precision'])) +print("Recall", np.mean(gbc_scores['test_recall'])) +print("F1", np.mean(gbc_scores['test_f1'])) +print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-gbc_scores['test_accuracy'], n_sl)[:n_sl])[::-1]) +print(f"Smallest {n_sl} ACC:", np.sort(np.partition(gbc_scores['test_accuracy'], n_sl)[:n_sl])) # %% [markdown] # ### LGBM Classifier @@ -353,12 +350,12 @@ lgbm_scores = cross_validate( scoring=('accuracy', 'precision', 'recall', 'f1') ) # %% jupyter={"source_hidden": true} -print("Acc", np.median(lgbm_scores['test_accuracy'])) -print("Precision", np.median(lgbm_scores['test_precision'])) -print("Recall", np.median(lgbm_scores['test_recall'])) -print("F1", np.median(lgbm_scores['test_f1'])) -print("Largest 5 ACC:", np.sort(-np.partition(-lgbm_scores['test_accuracy'], n)[:n])[::-1]) -print("Smallest 5 ACC:", np.sort(np.partition(lgbm_scores['test_accuracy'], n)[:n])) +print("Acc", np.mean(lgbm_scores['test_accuracy'])) +print("Precision", np.mean(lgbm_scores['test_precision'])) +print("Recall", np.mean(lgbm_scores['test_recall'])) +print("F1", np.mean(lgbm_scores['test_f1'])) +print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-lgbm_scores['test_accuracy'], n_sl)[:n_sl])[::-1]) +print(f"Smallest {n_sl} ACC:", np.sort(np.partition(lgbm_scores['test_accuracy'], n_sl)[:n_sl])) # %% [markdown] # ### XGBoost Classifier @@ -378,9 +375,9 @@ xgb_classifier_scores = cross_validate( scoring=('accuracy', 'precision', 'recall', 'f1') ) # %% jupyter={"source_hidden": true} -print("Acc", np.median(xgb_classifier_scores['test_accuracy'])) -print("Precision", np.median(xgb_classifier_scores['test_precision'])) -print("Recall", np.median(xgb_classifier_scores['test_recall'])) -print("F1", np.median(xgb_classifier_scores['test_f1'])) -print("Largest 5 ACC:", np.sort(-np.partition(-xgb_classifier_scores['test_accuracy'], n)[:n])[::-1]) -print("Smallest 5 ACC:", np.sort(np.partition(xgb_classifier_scores['test_accuracy'], n)[:n])) +print("Acc", np.mean(xgb_classifier_scores['test_accuracy'])) +print("Precision", np.mean(xgb_classifier_scores['test_precision'])) +print("Recall", np.mean(xgb_classifier_scores['test_recall'])) +print("F1", np.mean(xgb_classifier_scores['test_f1'])) +print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-xgb_classifier_scores['test_accuracy'], n_sl)[:n_sl])[::-1]) +print(f"Smallest {n_sl} ACC:", np.sort(np.partition(xgb_classifier_scores['test_accuracy'], n_sl)[:n_sl])) diff --git a/exploration/ml_pipeline_classification_with_clustering.py b/exploration/ml_pipeline_classification_with_clustering.py index 9d81b21..56edc90 100644 --- a/exploration/ml_pipeline_classification_with_clustering.py +++ b/exploration/ml_pipeline_classification_with_clustering.py @@ -49,26 +49,38 @@ import machine_learning.model # # RAPIDS models # %% [markdown] -# ## PANAS negative affect +# ## Set script's parameters +n_clusters = 5 # Number of clusters (could be regarded as a hyperparameter) +cv_method_str = 'logo' # logo, halflogo, 5kfold # Cross-validation method (could be regarded as a hyperparameter) +n_sl = 1 # Number of largest/smallest accuracies (of particular CV) outputs # %% jupyter={"source_hidden": true} model_input = pd.read_csv("../data/intradaily_30_min_all_targets/input_JCQ_job_demand_mean.csv") +index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"] -lime_cols = [col for col in model_input if col.startswith('limesurvey_demand')] +clust_col = model_input.set_index(index_columns).var().idxmax() # age is a col with the highest variance + +model_input.columns[list(model_input.columns).index('age'):-1] + +lime_cols = [col for col in model_input if col.startswith('limesurvey')] +lime_cols lime_col = 'limesurvey_demand_control_ratio' -model_input[lime_col].describe() +clust_col = lime_col + +model_input[clust_col].describe() + # %% jupyter={"source_hidden": true} -# Filter-out outlier rows by lime_col -model_input = model_input[(np.abs(stats.zscore(model_input[lime_col])) < 3)] +# Filter-out outlier rows by clust_col +model_input = model_input[(np.abs(stats.zscore(model_input[clust_col])) < 3)] -uniq = model_input[[lime_col, 'pid']].drop_duplicates().reset_index(drop=True) -plt.bar(uniq['pid'], uniq[lime_col]) +uniq = model_input[[clust_col, 'pid']].drop_duplicates().reset_index(drop=True) +plt.bar(uniq['pid'], uniq[clust_col]) # %% jupyter={"source_hidden": true} -# Get clusters by lime col & and merge the clusters to main df -km = KMeans(n_clusters=5).fit_predict(uniq.set_index('pid')) +# Get clusters by cluster col & and merge the clusters to main df +km = KMeans(n_clusters=n_clusters).fit_predict(uniq.set_index('pid')) np.unique(km, return_counts=True) uniq['cluster'] = km uniq @@ -76,12 +88,59 @@ uniq model_input = model_input.merge(uniq[['pid', 'cluster']]) # %% jupyter={"source_hidden": true} -index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"] model_input.set_index(index_columns, inplace=True) # %% jupyter={"source_hidden": true} +# Create dict with classification ml models +cmodels = { + 'dummy_classifier': { + 'model': DummyClassifier(strategy="most_frequent"), + 'metrics': [0, 0, 0, 0] + }, + 'logistic_regression': { + 'model': linear_model.LogisticRegression(), + 'metrics': [0, 0, 0, 0] + }, + 'support_vector_machine': { + 'model': svm.SVC(), + 'metrics': [0, 0, 0, 0] + }, + 'gaussian_naive_bayes': { + 'model': naive_bayes.GaussianNB(), + 'metrics': [0, 0, 0, 0] + }, + 'stochastic_gradient_descent_classifier': { + 'model': linear_model.SGDClassifier(), + 'metrics': [0, 0, 0, 0] + }, + 'knn': { + 'model': neighbors.KNeighborsClassifier(), + 'metrics': [0, 0, 0, 0] + }, + 'decision_tree': { + 'model': tree.DecisionTreeClassifier(), + 'metrics': [0, 0, 0, 0] + }, + 'random_forest_classifier': { + 'model': ensemble.RandomForestClassifier(), + 'metrics': [0, 0, 0, 0] + }, + 'gradient_boosting_classifier': { + 'model': ensemble.GradientBoostingClassifier(), + 'metrics': [0, 0, 0, 0] + }, + 'lgbm_classifier': { + 'model': LGBMClassifier(), + 'metrics': [0, 0, 0, 0] + }, + 'XGBoost_classifier': { + 'model': xg.sklearn.XGBClassifier(), + 'metrics': [0, 0, 0, 0] + } +} -for k in range(5): +# %% jupyter={"source_hidden": true} +for k in range(n_clusters): model_input_subset = model_input[model_input["cluster"] == k].copy() bins = [-10, -1, 1, 10] # bins for z-scored targets model_input_subset.loc[:, 'target'] = \ @@ -92,8 +151,6 @@ for k in range(5): model_input_subset['target'].value_counts() - - cv_method_str = 'logo' # logo, halflogo, 5kfold if cv_method_str == 'halflogo': model_input_subset['pid_index'] = model_input_subset.groupby('pid').cumcount() model_input_subset['pid_count'] = model_input_subset.groupby('pid')['pid'].transform('count') @@ -134,45 +191,42 @@ for k in range(5): groups=data_groups, ) - n = 3 - imputer = SimpleImputer(missing_values=np.nan, strategy='median') - # Create dict with classification ml models - cmodels = { - 'dummy_classifier': DummyClassifier(strategy="most_frequent"), - 'logistic_regression': linear_model.LogisticRegression(), - 'support_vector_machine': svm.SVC(), - 'gaussian_naive_bayes': naive_bayes.GaussianNB(), - 'stochastic_gradient_descent_classifier': linear_model.SGDClassifier(), - 'knn': neighbors.KNeighborsClassifier(), - 'decision_tree': tree.DecisionTreeClassifier(), - 'random_forest_classifier': ensemble.RandomForestClassifier(), - 'gradient_boosting_classifier': ensemble.GradientBoostingClassifier(), - 'lgbm_classifier': LGBMClassifier(), - 'XGBoost_classifier': xg.sklearn.XGBClassifier() - } - for model_title, model in cmodels.items(): - + classifier = cross_validate( - model, + model['model'], X=imputer.fit_transform(train_x), y=data_y, groups=data_groups, cv=cv_method, n_jobs=-1, error_score='raise', - scoring=('accuracy', 'average_precision', 'recall', 'f1') + scoring=('accuracy', 'precision', 'recall', 'f1') ) print("\n-------------------------------------\n") print("Current cluster:", k, end="\n") print("Current model:", model_title, end="\n") - print("Acc", np.median(classifier['test_accuracy'])) - print("Precision", np.median(classifier['test_average_precision'])) - print("Recall", np.median(classifier['test_recall'])) - print("F1", np.median(classifier['test_f1'])) - print("Largest 5 ACC:", np.sort(-np.partition(-classifier['test_accuracy'], n)[:n])[::-1]) - print("Smallest 5 ACC:", np.sort(np.partition(classifier['test_accuracy'], n)[:n])) -# %% + print("Acc", np.mean(classifier['test_accuracy'])) + print("Precision", np.mean(classifier['test_precision'])) + print("Recall", np.mean(classifier['test_recall'])) + print("F1", np.mean(classifier['test_f1'])) + print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-classifier['test_accuracy'], n_sl)[:n_sl])[::-1]) + print(f"Smallest {n_sl} ACC:", np.sort(np.partition(classifier['test_accuracy'], n_sl)[:n_sl])) + + cmodels[model_title]['metrics'][0] += np.mean(classifier['test_accuracy']) + cmodels[model_title]['metrics'][1] += np.mean(classifier['test_precision']) + cmodels[model_title]['metrics'][2] += np.mean(classifier['test_accuracy']) + cmodels[model_title]['metrics'][3] += np.mean(classifier['test_f1']) + +# %% jupyter={"source_hidden": true} +# Get overall results +for model_title, model in cmodels.items(): + print("\n************************************\n") + print("Current model:", model_title, end="\n") + print("Acc", model['metrics'][0]/n_clusters) + print("Precision", model['metrics'][1]/n_clusters) + print("Recall", model['metrics'][2]/n_clusters) + print("F1", model['metrics'][3]/n_clusters)