Implement feature selection methods (WIP).
Primoz
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f672709ea6
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10ca47583c
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@ -7,13 +7,12 @@ import pandas as pd
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from sklearn.feature_selection import SequentialFeatureSelector
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from sklearn.naive_bayes import GaussianNB
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from sklearn.linear_model import Lasso
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""" Feature selection pipeline: a methods that can be used in the wrapper metod alongside other wrapper contents (hyperparameter tuning etc.).
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(1) Establish methods for each of the steps in feature selection protocol:
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(a) feature selection inside specific sensors (sklearn method): returns most important features from all sensors
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(b) feature selection between "tuned" sensors: returns filtered sensors, containing most important features retured with (a)
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(1) Establish methods for each of the steps in feature selection protocol.
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(2) Ensure that above methods are given only a part of data and use appropriate random seeds - to later simulate use case in production.
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(3) Implement a method which gives graphical exploration of (1) (a) and (b) steps of the feature selection.
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(4) Prepare a core method that can be fit into a wrapper (see sklearn wrapper methods) and integrates methods from (1)
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@ -23,19 +22,196 @@ from sklearn.naive_bayes import GaussianNB
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class FeatureSelection:
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def __init__(self, X_train, X_test, y_train, y_test): # TODO: what about leave-one-subject-out CV?
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pass
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pass # TODO....
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def select_best_feature(df, features, method="remove", ml_type="classification", metric="recall", stored_features=[]):
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"""The method selects the best feature by testing the prediction on the feature set with or without the current feature.
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The "remove" method removes a particular feature and predicts on the test set without it. The "add" method adds a particulat
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feature to the previously established feature set (stored_features). The best feature is selected dependent on the metric
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specified as a parameter.
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def within_sensors_feature_selection(estimator, scoring, tol):
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features_list = []
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Args:
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df (DataFrame): Input data on which the predictions will be made.
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features (list): List of features to select the best/worst from
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method (str, optional): remove or add features. Defaults to "remove".
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ml_type (str, optional): Either classification or regression ml problem controls the ML algorithm and metric. Defaults to "classification".
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metric (str, optional): Selected metric with which the best/worst feature will be determined. Defaults to "recall".
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stored_features (list, optional): In case if method is 'add', stored features refer to the features that had been previously added. Defaults to [].
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nb = GaussianNB()
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sfs = SequentialFeatureSelector(nb, n_features_to_select='auto', tol=0.02) # Can set n_features to an absolute value -> then remove tol parameter.
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Raises:
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ValueError: Raises if classification or regression metrics are not recognised if a specific ml_type is selected.
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ValueError: If unknown ml_type is chosen.
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Returns:
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tuple: name of the best feature, best feature score, best feature score standard deviation.
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"""
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best_feature = None
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if ml_type == "classification" and metric not in ['accuracy', 'precision', 'recall', 'f1']:
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raise ValueError("Classification metric not recognized. Please choose 'accuracy', 'precision', 'recall' and/or 'f1'")
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elif ml_type == "regression" and metric not in ['r2']:
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raise ValueError("Regression metric not recognized. Please choose 'r2'")
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for feat in features:
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if method == "remove":
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pred_features = [col for col in df.columns if feat != col] # All but feat
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elif method == "add":
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pred_features = [feat] + stored_features # Feat with stored features
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X, y = df.drop(columns=['target', 'pid'])[pred_features], df['target']
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if ml_type == "classification":
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nb = GaussianNB()
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model_cv = cross_validate(
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nb,
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X=X,
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y=y,
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cv=StratifiedKFold(n_splits=5, shuffle=True),
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n_jobs=-1,
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", message="Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.")
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return features_list
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if metric == "accuracy":
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acc = np.mean(model_cv['test_accuracy'])
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acc_std = np.std(model_cv['test_accuracy'])
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if not best_feature or (acc > best_metric_score):
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best_feature = feat
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best_metric_score = acc
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best_metric_score_std = acc_std
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elif metric == "precision":
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prec = np.mean(model_cv['test_precision'])
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prec_std = np.std(model_cv['test_precision'])
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if not best_feature or (prec > best_metric_score):
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best_feature = feat
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best_metric_score = prec
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best_metric_score_std = prec_std
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elif metric == "recall":
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rec = np.mean(model_cv['test_recall'])
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rec_std = np.std(model_cv['test_recall'])
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if not best_feature or (rec > best_metric_score):
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best_feature = feat
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best_metric_score = rec
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best_metric_score_std = rec_std
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else:
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f1 = np.mean(model_cv['test_f1'])
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f1_std = np.std(model_cv['test_f1'])
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if not best_feature or (f1 > best_metric_score):
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best_feature = feat
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best_metric_score = f1
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best_metric_score_std = f1_std
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elif ml_type == "regression":
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lass = Lasso()
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model_cv = cross_validate(
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lass,
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X=X,
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y=y,
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cv=StratifiedKFold(n_splits=5, shuffle=True),
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n_jobs=-1,
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scoring=('r2')
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)
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def between_sensors_feature_selection():
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if metric == "r2":
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r2 = np.mean(model_cv['test_r2'])
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r2_std = np.std(model_cv['test_r2'])
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if not best_feature or (r2 > best_metric_score):
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best_feature = feat
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best_metric_score = r2
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best_metric_score_std = r2_std
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else:
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raise ValueError("ML type not yet implemented!")
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return best_feature, best_metric_score, best_metric_score_std
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def select_features(df, n_min=20, n_max=50, method="remove", n_not_improve=10):
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n_features = df.shape[1] - 2 # -2 beacause pid and target are not considered
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if n_max > n_features:
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n_max = n_features
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if n_min > n_features:
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raise ValueError("The number of features in the dataframe must be at least as n_min-1 parameter.")
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if n_max < n_min:
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raise ValueError("n_max parameter needs to be greater than or equal to n_min parameter.")
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features = df.columns.tolist()
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features.remove("pid")
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features.remove("target")
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feature_importance = []
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if method == "remove":
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for i in reversed(range(n_features)):
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best_feature, best_metric_score, best_metric_score_std = \
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self.select_best_feature(df, features, method=method, ml_type="classification", metric="recall")
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feature_importance.append(tuple(i+1, best_feature, best_metric_score, best_metric_score_std))
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features.remove(best_feature)
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feature_importance_df = pd.DataFrame(feature_importance, columns=['i', 'name', 'metric', 'metric_sd'])
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# Selekcijski kriterij značilk v rangu max-min
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# Npr. izbira najboljšega score-a v tem rangu. Ali pa dokler se v tem rangu score zvišuje za 0.0X, ko se ne izberi tisti set značilk.
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# Set značilk se bo izbral od i=1 do i=index_izbrane_značilke
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# "Tipping point" značilka mora biti v rangu max-min
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selection_area = feature_importance_df[(feature_importance_df["i"] >= n_min+1) & (feature_importance_df["i"] <= n_max)]
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selection_area.set_index(["i", "name"], inplace=True)
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diffrences = selection_area.diff()
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diffrences.dropna(how='any', inplace=True)
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# Morda tudi komulativna sumacija? Kjer se preprosto index z najvišjo vrednostjo
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cumulative_sumation = diffrences.cumsum()
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tipping_feature_indx_1 = cumulative_sumation.idxmax()["metric"]
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# Zelo konzervativna metoda, ki ob prvem neizboljšanjem rezultata preneha z iskanjem boljše alternative
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tipping_feature_indx_2 = None
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for indx, row in diffrences.iterrows():
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if row["metric"] > 0:
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tipping_feature_indx_2 = indx
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else:
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break
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# Metoda, ki pusti n_not_improve značilkam, da premagajo dosedajno najboljši score
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tipping_feature_indx_3 = None
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cum_sum_score = 0
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i_worse = 0
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# TODO: morda bi bilo smisleno združiti diff, cumsum in scores stolpce ...
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for indx, row in selection_area.iterrows():
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if row["metric"] > 0:
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tipping_feature_indx_3 = indx
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cum_sum_score += row["metric"]
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i_worse = 0
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else:
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i_worse += 1
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if i_worse == n_not_improve:
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break
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def make_predictions_with_features(df, groups_substrings, include_group=True, with_cols=[], print_flag=False):
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pass
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def vizualize_feature_selection_process():
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