Improve general ml classification pipeline script.
Primoz
parent
40029a8205
commit
183758cd37
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@ -52,25 +52,20 @@ import machine_learning.model
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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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# %% jupyter={"source_hidden": true}
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bins = [-4, -1, 1, 4] # 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)
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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}
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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'].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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# %% 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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#if "pid" in model_input.columns:
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# index_columns.append("pid")
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model_input.set_index(index_columns, inplace=True)
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# %% jupyter={"source_hidden": true}
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cv_method = '5kfold'
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if cv_method == 'logo':
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data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
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else:
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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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@ -78,6 +73,9 @@ else:
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model_input["pid_half"] = model_input["pid"] + "_" + model_input["pid_index"].astype(int).astype(str)
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data_x, data_y, data_groups = model_input.drop(["target", "pid", "pid_index", "pid_half"], axis=1), model_input["target"], model_input["pid_half"]
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else:
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data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
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# %% jupyter={"source_hidden": true}
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categorical_feature_colnames = ["gender", "startlanguage"]
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@ -100,19 +98,21 @@ train_x = pd.concat([numerical_features, categorical_features], axis=1)
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train_x.dtypes
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# %% jupyter={"source_hidden": true}
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logo = LeaveOneGroupOut()
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logo.get_n_splits(
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cv_method = None # Defaults to 5 k-folds in cross_validate method
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if cv_method_str == 'logo' or cv_method_str == 'half_logo':
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cv_method = LeaveOneGroupOut()
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cv_method.get_n_splits(
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train_x,
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data_y,
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groups=data_groups,
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)
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# Defaults to 5 k-folds in cross_validate method
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if cv_method != 'logo' and cv_method != 'half_logo':
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logo = None
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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='mean')
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imputer = SimpleImputer(missing_values=np.nan, strategy='median')
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# %% [markdown]
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# ### Baseline: Dummy Classifier (most frequent)
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@ -124,8 +124,9 @@ dummy_classifier = cross_validate(
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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cv=cv_method,
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n_jobs=-1,
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error_score='raise',
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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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@ -133,6 +134,8 @@ 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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# %% [markdown]
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# ### Logistic Regression
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@ -146,7 +149,7 @@ log_reg_scores = cross_validate(
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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cv=cv_method,
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n_jobs=-1,
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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@ -155,6 +158,8 @@ 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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# %% [markdown]
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# ### Support Vector Machine
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@ -168,7 +173,7 @@ svc_scores = cross_validate(
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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cv=cv_method,
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n_jobs=-1,
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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@ -177,6 +182,8 @@ 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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# %% [markdown]
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# ### Gaussian Naive Bayes
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@ -190,8 +197,9 @@ gaussian_nb_scores = cross_validate(
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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cv=cv_method,
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n_jobs=-1,
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error_score='raise',
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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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@ -199,6 +207,8 @@ 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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# %% [markdown]
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# ### Stochastic Gradient Descent Classifier
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@ -212,8 +222,9 @@ sgdc_scores = cross_validate(
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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cv=cv_method,
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n_jobs=-1,
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error_score='raise',
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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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@ -221,6 +232,8 @@ 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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# %% [markdown]
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# ### K-nearest neighbors
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@ -229,21 +242,23 @@ print("F1", np.median(sgdc_scores['test_f1']))
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knn = neighbors.KNeighborsClassifier()
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# %% jupyter={"source_hidden": true}
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knn_scores = cross_validate( # Nekaj ne funkcionira pravilno
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knn_scores = cross_validate(
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knn,
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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cv=cv_method,
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n_jobs=-1,
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error_score='raise',
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scoring=('accuracy', 'precision', 'recall', 'f1')
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# error_score='raise'
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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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# %% [markdown]
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# ### Decision Tree
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@ -257,8 +272,9 @@ dtree_scores = cross_validate(
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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cv=cv_method,
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n_jobs=-1,
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error_score='raise',
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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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@ -266,6 +282,8 @@ 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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# %% [markdown]
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# ### Random Forest Classifier
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@ -279,8 +297,9 @@ rfc_scores = cross_validate(
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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cv=cv_method,
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n_jobs=-1,
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error_score='raise',
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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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@ -288,6 +307,8 @@ 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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# %% [markdown]
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# ### Gradient Boosting Classifier
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@ -301,8 +322,9 @@ gbc_scores = cross_validate(
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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cv=cv_method,
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n_jobs=-1,
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error_score='raise',
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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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@ -310,6 +332,8 @@ 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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# %% [markdown]
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# ### LGBM Classifier
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@ -323,8 +347,9 @@ lgbm_scores = cross_validate(
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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cv=cv_method,
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n_jobs=-1,
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error_score='raise',
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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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@ -332,6 +357,8 @@ 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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# %% [markdown]
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# ### XGBoost Classifier
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@ -345,8 +372,9 @@ xgb_classifier_scores = cross_validate(
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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cv=cv_method,
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n_jobs=-1,
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error_score='raise',
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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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@ -354,3 +382,5 @@ 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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