Automize clustering classification logic and add parameters at the begining of the scripts. General changes and improvements.
Primoz
parent
ddde80b421
commit
218b684514
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@ -46,7 +46,9 @@ import machine_learning.model
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# # RAPIDS models
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# %% [markdown]
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# ## PANAS negative affect
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# ## Set script's parameters
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cv_method_str = 'logo' # logo, halflogo, 5kfold # Cross-validation method (could be regarded as a hyperparameter)
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n_sl = 1 # Number of largest/smallest accuracies (of particular CV) outputs
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# %% jupyter={"source_hidden": true}
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model_input = pd.read_csv("../data/intradaily_30_min_all_targets/input_JCQ_job_demand_mean.csv")
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@ -57,14 +59,13 @@ model_input.set_index(index_columns, inplace=True)
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# %% jupyter={"source_hidden": true}
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bins = [-10, -1, 1, 10] # bins for z-scored targets
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model_input['target'], edges = pd.cut(model_input.target, bins=bins, labels=['low', 'medium', 'high'], retbins=True, right=False) #['low', 'medium', 'high']
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model_input['target'], edges = pd.cut(model_input.target, bins=bins, labels=['low', 'medium', 'high'], retbins=True, right=True) #['low', 'medium', 'high']
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model_input['target'].value_counts(), edges
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model_input = model_input[model_input['target'] != "medium"]
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model_input['target'] = model_input['target'].astype(str).apply(lambda x: 0 if x == "low" else 1)
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model_input['target'].value_counts()
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cv_method_str = 'logo' # logo, halflogo, 5kfold
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if cv_method_str == 'halflogo':
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model_input['pid_index'] = model_input.groupby('pid').cumcount()
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model_input['pid_count'] = model_input.groupby('pid')['pid'].transform('count')
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@ -106,10 +107,6 @@ if cv_method_str == 'logo' or cv_method_str == 'half_logo':
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data_y,
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groups=data_groups,
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)
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# %% jupyter={"source_hidden": true}
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# %% [markdown]
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# ### Set n for nlargest and nsmallest
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n = 5
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# %% jupyter={"source_hidden": true}
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imputer = SimpleImputer(missing_values=np.nan, strategy='median')
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@ -130,12 +127,12 @@ dummy_classifier = cross_validate(
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scoring=('accuracy', 'average_precision', 'recall', 'f1')
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)
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# %% jupyter={"source_hidden": true}
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print("Acc", np.median(dummy_classifier['test_accuracy']))
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print("Precision", np.median(dummy_classifier['test_average_precision']))
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print("Recall", np.median(dummy_classifier['test_recall']))
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print("F1", np.median(dummy_classifier['test_f1']))
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print("Largest 5 ACC:", np.sort(-np.partition(-dummy_classifier['test_accuracy'], n)[:n])[::-1])
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print("Smallest 5 ACC:", np.sort(np.partition(dummy_classifier['test_accuracy'], n)[:n]))
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print("Acc", np.mean(dummy_classifier['test_accuracy']))
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print("Precision", np.mean(dummy_classifier['test_average_precision']))
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print("Recall", np.mean(dummy_classifier['test_recall']))
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print("F1", np.mean(dummy_classifier['test_f1']))
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print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-dummy_classifier['test_accuracy'], n_sl)[:n_sl])[::-1])
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print(f"Smallest {n_sl} ACC:", np.sort(np.partition(dummy_classifier['test_accuracy'], n_sl)[:n_sl]))
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# %% [markdown]
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# ### Logistic Regression
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@ -154,12 +151,12 @@ log_reg_scores = cross_validate(
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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# %% jupyter={"source_hidden": true}
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print("Acc", np.median(log_reg_scores['test_accuracy']))
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print("Precision", np.median(log_reg_scores['test_precision']))
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print("Recall", np.median(log_reg_scores['test_recall']))
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print("F1", np.median(log_reg_scores['test_f1']))
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print("Largest 5 ACC:", np.sort(-np.partition(-log_reg_scores['test_accuracy'], n)[:n])[::-1])
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print("Smallest 5 ACC:", np.sort(np.partition(log_reg_scores['test_accuracy'], n)[:n]))
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print("Acc", np.mean(log_reg_scores['test_accuracy']))
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print("Precision", np.mean(log_reg_scores['test_precision']))
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print("Recall", np.mean(log_reg_scores['test_recall']))
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print("F1", np.mean(log_reg_scores['test_f1']))
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print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-log_reg_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
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print(f"Smallest {n_sl} ACC:", np.sort(np.partition(log_reg_scores['test_accuracy'], n_sl)[:n_sl]))
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# %% [markdown]
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# ### Support Vector Machine
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@ -178,12 +175,12 @@ svc_scores = cross_validate(
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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# %% jupyter={"source_hidden": true}
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print("Acc", np.median(svc_scores['test_accuracy']))
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print("Precision", np.median(svc_scores['test_precision']))
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print("Recall", np.median(svc_scores['test_recall']))
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print("F1", np.median(svc_scores['test_f1']))
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print("Largest 5 ACC:", np.sort(-np.partition(-svc_scores['test_accuracy'], n)[:n])[::-1])
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print("Smallest 5 ACC:", np.sort(np.partition(svc_scores['test_accuracy'], n)[:n]))
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print("Acc", np.mean(svc_scores['test_accuracy']))
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print("Precision", np.mean(svc_scores['test_precision']))
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print("Recall", np.mean(svc_scores['test_recall']))
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print("F1", np.mean(svc_scores['test_f1']))
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print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-svc_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
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print(f"Smallest {n_sl} ACC:", np.sort(np.partition(svc_scores['test_accuracy'], n_sl)[:n_sl]))
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# %% [markdown]
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# ### Gaussian Naive Bayes
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@ -203,12 +200,12 @@ gaussian_nb_scores = cross_validate(
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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# %% jupyter={"source_hidden": true}
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print("Acc", np.median(gaussian_nb_scores['test_accuracy']))
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print("Precision", np.median(gaussian_nb_scores['test_precision']))
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print("Recall", np.median(gaussian_nb_scores['test_recall']))
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print("F1", np.median(gaussian_nb_scores['test_f1']))
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print("Largest 5 ACC:", np.sort(-np.partition(-gaussian_nb_scores['test_accuracy'], n)[:n])[::-1])
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print("Smallest 5 ACC:", np.sort(np.partition(gaussian_nb_scores['test_accuracy'], n)[:n]))
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print("Acc", np.mean(gaussian_nb_scores['test_accuracy']))
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print("Precision", np.mean(gaussian_nb_scores['test_precision']))
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print("Recall", np.mean(gaussian_nb_scores['test_recall']))
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print("F1", np.mean(gaussian_nb_scores['test_f1']))
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print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-gaussian_nb_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
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print(f"Smallest {n_sl} ACC:", np.sort(np.partition(gaussian_nb_scores['test_accuracy'], n_sl)[:n_sl]))
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# %% [markdown]
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# ### Stochastic Gradient Descent Classifier
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@ -228,12 +225,12 @@ sgdc_scores = cross_validate(
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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# %% jupyter={"source_hidden": true}
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print("Acc", np.median(sgdc_scores['test_accuracy']))
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print("Precision", np.median(sgdc_scores['test_precision']))
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print("Recall", np.median(sgdc_scores['test_recall']))
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print("F1", np.median(sgdc_scores['test_f1']))
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print("Largest 5 ACC:", np.sort(-np.partition(-sgdc_scores['test_accuracy'], n)[:n])[::-1])
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print("Smallest 5 ACC:", np.sort(np.partition(sgdc_scores['test_accuracy'], n)[:n]))
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print("Acc", np.mean(sgdc_scores['test_accuracy']))
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print("Precision", np.mean(sgdc_scores['test_precision']))
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print("Recall", np.mean(sgdc_scores['test_recall']))
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print("F1", np.mean(sgdc_scores['test_f1']))
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print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-sgdc_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
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print(f"Smallest {n_sl} ACC:", np.sort(np.partition(sgdc_scores['test_accuracy'], n_sl)[:n_sl]))
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# %% [markdown]
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# ### K-nearest neighbors
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@ -253,12 +250,12 @@ knn_scores = cross_validate(
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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# %% jupyter={"source_hidden": true}
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print("Acc", np.median(knn_scores['test_accuracy']))
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print("Precision", np.median(knn_scores['test_precision']))
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print("Recall", np.median(knn_scores['test_recall']))
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print("F1", np.median(knn_scores['test_f1']))
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print("Largest 5 ACC:", np.sort(-np.partition(-knn_scores['test_accuracy'], n)[:n])[::-1])
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print("Smallest 5 ACC:", np.sort(np.partition(knn_scores['test_accuracy'], n)[:n]))
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print("Acc", np.mean(knn_scores['test_accuracy']))
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print("Precision", np.mean(knn_scores['test_precision']))
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print("Recall", np.mean(knn_scores['test_recall']))
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print("F1", np.mean(knn_scores['test_f1']))
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print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-knn_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
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print(f"Smallest {n_sl} ACC:", np.sort(np.partition(knn_scores['test_accuracy'], n_sl)[:n_sl]))
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# %% [markdown]
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# ### Decision Tree
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@ -278,12 +275,12 @@ dtree_scores = cross_validate(
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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# %% jupyter={"source_hidden": true}
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print("Acc", np.median(dtree_scores['test_accuracy']))
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print("Precision", np.median(dtree_scores['test_precision']))
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print("Recall", np.median(dtree_scores['test_recall']))
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print("F1", np.median(dtree_scores['test_f1']))
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print("Largest 5 ACC:", np.sort(-np.partition(-dtree_scores['test_accuracy'], n)[:n])[::-1])
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print("Smallest 5 ACC:", np.sort(np.partition(dtree_scores['test_accuracy'], n)[:n]))
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print("Acc", np.mean(dtree_scores['test_accuracy']))
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print("Precision", np.mean(dtree_scores['test_precision']))
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print("Recall", np.mean(dtree_scores['test_recall']))
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print("F1", np.mean(dtree_scores['test_f1']))
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print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-dtree_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
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print(f"Smallest {n_sl} ACC:", np.sort(np.partition(dtree_scores['test_accuracy'], n_sl)[:n_sl]))
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# %% [markdown]
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# ### Random Forest Classifier
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@ -303,12 +300,12 @@ rfc_scores = cross_validate(
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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# %% jupyter={"source_hidden": true}
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print("Acc", np.median(rfc_scores['test_accuracy']))
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print("Precision", np.median(rfc_scores['test_precision']))
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print("Recall", np.median(rfc_scores['test_recall']))
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print("F1", np.median(rfc_scores['test_f1']))
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print("Largest 5 ACC:", np.sort(-np.partition(-rfc_scores['test_accuracy'], n)[:n])[::-1])
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print("Smallest 5 ACC:", np.sort(np.partition(rfc_scores['test_accuracy'], n)[:n]))
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print("Acc", np.mean(rfc_scores['test_accuracy']))
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print("Precision", np.mean(rfc_scores['test_precision']))
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print("Recall", np.mean(rfc_scores['test_recall']))
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print("F1", np.mean(rfc_scores['test_f1']))
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print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-rfc_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
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print(f"Smallest {n_sl} ACC:", np.sort(np.partition(rfc_scores['test_accuracy'], n_sl)[:n_sl]))
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# %% [markdown]
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# ### Gradient Boosting Classifier
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@ -328,12 +325,12 @@ gbc_scores = cross_validate(
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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# %% jupyter={"source_hidden": true}
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print("Acc", np.median(gbc_scores['test_accuracy']))
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print("Precision", np.median(gbc_scores['test_precision']))
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print("Recall", np.median(gbc_scores['test_recall']))
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print("F1", np.median(gbc_scores['test_f1']))
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print("Largest 5 ACC:", np.sort(-np.partition(-gbc_scores['test_accuracy'], n)[:n])[::-1])
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print("Smallest 5 ACC:", np.sort(np.partition(gbc_scores['test_accuracy'], n)[:n]))
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print("Acc", np.mean(gbc_scores['test_accuracy']))
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print("Precision", np.mean(gbc_scores['test_precision']))
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print("Recall", np.mean(gbc_scores['test_recall']))
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print("F1", np.mean(gbc_scores['test_f1']))
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print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-gbc_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
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print(f"Smallest {n_sl} ACC:", np.sort(np.partition(gbc_scores['test_accuracy'], n_sl)[:n_sl]))
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# %% [markdown]
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# ### LGBM Classifier
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@ -353,12 +350,12 @@ lgbm_scores = cross_validate(
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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# %% jupyter={"source_hidden": true}
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print("Acc", np.median(lgbm_scores['test_accuracy']))
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print("Precision", np.median(lgbm_scores['test_precision']))
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print("Recall", np.median(lgbm_scores['test_recall']))
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print("F1", np.median(lgbm_scores['test_f1']))
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print("Largest 5 ACC:", np.sort(-np.partition(-lgbm_scores['test_accuracy'], n)[:n])[::-1])
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print("Smallest 5 ACC:", np.sort(np.partition(lgbm_scores['test_accuracy'], n)[:n]))
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print("Acc", np.mean(lgbm_scores['test_accuracy']))
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print("Precision", np.mean(lgbm_scores['test_precision']))
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print("Recall", np.mean(lgbm_scores['test_recall']))
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print("F1", np.mean(lgbm_scores['test_f1']))
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print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-lgbm_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
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print(f"Smallest {n_sl} ACC:", np.sort(np.partition(lgbm_scores['test_accuracy'], n_sl)[:n_sl]))
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# %% [markdown]
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# ### XGBoost Classifier
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@ -378,9 +375,9 @@ xgb_classifier_scores = cross_validate(
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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# %% jupyter={"source_hidden": true}
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print("Acc", np.median(xgb_classifier_scores['test_accuracy']))
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print("Precision", np.median(xgb_classifier_scores['test_precision']))
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print("Recall", np.median(xgb_classifier_scores['test_recall']))
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print("F1", np.median(xgb_classifier_scores['test_f1']))
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print("Largest 5 ACC:", np.sort(-np.partition(-xgb_classifier_scores['test_accuracy'], n)[:n])[::-1])
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print("Smallest 5 ACC:", np.sort(np.partition(xgb_classifier_scores['test_accuracy'], n)[:n]))
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print("Acc", np.mean(xgb_classifier_scores['test_accuracy']))
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print("Precision", np.mean(xgb_classifier_scores['test_precision']))
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print("Recall", np.mean(xgb_classifier_scores['test_recall']))
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print("F1", np.mean(xgb_classifier_scores['test_f1']))
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print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-xgb_classifier_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
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print(f"Smallest {n_sl} ACC:", np.sort(np.partition(xgb_classifier_scores['test_accuracy'], n_sl)[:n_sl]))
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@ -49,26 +49,38 @@ import machine_learning.model
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# # RAPIDS models
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# %% [markdown]
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# ## PANAS negative affect
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# ## Set script's parameters
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n_clusters = 5 # Number of clusters (could be regarded as a hyperparameter)
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cv_method_str = 'logo' # logo, halflogo, 5kfold # Cross-validation method (could be regarded as a hyperparameter)
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n_sl = 1 # Number of largest/smallest accuracies (of particular CV) outputs
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# %% jupyter={"source_hidden": true}
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model_input = pd.read_csv("../data/intradaily_30_min_all_targets/input_JCQ_job_demand_mean.csv")
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index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
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lime_cols = [col for col in model_input if col.startswith('limesurvey_demand')]
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clust_col = model_input.set_index(index_columns).var().idxmax() # age is a col with the highest variance
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model_input.columns[list(model_input.columns).index('age'):-1]
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lime_cols = [col for col in model_input if col.startswith('limesurvey')]
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lime_cols
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lime_col = 'limesurvey_demand_control_ratio'
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model_input[lime_col].describe()
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clust_col = lime_col
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model_input[clust_col].describe()
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# %% jupyter={"source_hidden": true}
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# Filter-out outlier rows by lime_col
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model_input = model_input[(np.abs(stats.zscore(model_input[lime_col])) < 3)]
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# Filter-out outlier rows by clust_col
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model_input = model_input[(np.abs(stats.zscore(model_input[clust_col])) < 3)]
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uniq = model_input[[lime_col, 'pid']].drop_duplicates().reset_index(drop=True)
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plt.bar(uniq['pid'], uniq[lime_col])
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uniq = model_input[[clust_col, 'pid']].drop_duplicates().reset_index(drop=True)
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plt.bar(uniq['pid'], uniq[clust_col])
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# %% jupyter={"source_hidden": true}
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# Get clusters by lime col & and merge the clusters to main df
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km = KMeans(n_clusters=5).fit_predict(uniq.set_index('pid'))
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# Get clusters by cluster col & and merge the clusters to main df
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km = KMeans(n_clusters=n_clusters).fit_predict(uniq.set_index('pid'))
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np.unique(km, return_counts=True)
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uniq['cluster'] = km
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uniq
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@ -76,12 +88,59 @@ uniq
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model_input = model_input.merge(uniq[['pid', 'cluster']])
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# %% jupyter={"source_hidden": true}
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index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
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model_input.set_index(index_columns, inplace=True)
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# %% 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)
|
||||
|
|
|
|||
Loading…
Reference in New Issue