271 lines
13 KiB
Python
271 lines
13 KiB
Python
import os
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import sys
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import warnings
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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from sklearn.feature_selection import SelectKBest, f_classif, mutual_info_classif, f_regression
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from sklearn.model_selection import cross_validate, StratifiedKFold, GroupKFold
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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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(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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"""
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class FeatureSelection:
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def __init__(self, X, y, groups):
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self.X = X
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self.y = y
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self.groups = groups
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def select_best_feature(self, 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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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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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 self.X.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 = self.X[pred_features].copy()
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if self.groups is not None:
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cv = GroupKFold(n_splits=5)
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else:
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cv = StratifiedKFold(n_splits=5, shuffle=True)
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# See link about scoring for multiclassfication
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# http://iamirmasoud.com/2022/06/19/understanding-micro-macro-and-weighted-averages-for-scikit-learn-metrics-in-multi-class-classification-with-example/
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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=self.y,
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cv=cv,
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groups=self.groups,
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n_jobs=-1,
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scoring=(metric)
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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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metric_score = np.nanmean(model_cv[f'test_{metric}'])
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metric_score_std = np.nanstd(model_cv[f'test_{metric}'])
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if not best_feature or (metric_score > best_metric_score):
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best_feature = feat
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best_metric_score = metric_score
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best_metric_score_std = metric_score_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=cv,
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groups=self.groups,
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n_jobs=-1,
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scoring=('r2')
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)
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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(self, n_min=20, n_max=50, k=100, method="remove", ml_type="classification_bin", metric="recall", n_tolerance=10):
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"""This method selects a set of features and returns them as a list. It returns number of features
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determined in the interval of [n_min, n_max].
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The method consists of two steps:
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(1) The method uses sklearn kBest method which selects k best features dependent on the ml_type parameter.
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(2) The sequential features removal procedure is executed. Using the remaing features from (1).
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The best score is detected using a removal procedure. The procedure sequentially removes the features
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that attribute the least to the choosen evaluation metric. If in this sequence the score ML score is
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improved the next feature is remove otherwise there is a tolerance criteria (n_tolerance)
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with which the next n removed features are inspected whether currently best score is improved.
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Args:
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n_min (int, optional): Minimal amount of features returned.
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n_max (int, optional): Maximal amount of features returned.
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k (int, optional): Determines the k in the k-best features method.
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ml_type(str, optional): Type of ML problem. Currently implemented options:
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classification_bin, classification_multi, and regression_
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method (str, optional): "remove" or "add" features. Defaults to "remove".
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n_tolerance (int, optional): If the best score is not improved in n that is specified by this parameter
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the method returns index of feature with current best score as a tipping point feature.
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Returns:
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list: list of selected features
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"""
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n_features = self.X.shape[1]
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if n_max >= n_features:
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n_max = n_features-1 # The algorithm removes at least one feature
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if k < n_max:
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raise ValueError("The k parameter needs to be lower than the n_max parameter.")
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# Select k-best feature dependent on the type of ML task
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ml_type = ml_type.split("_")
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if ml_type[0] == "classification":
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if ml_type[1] == "bin":
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selector = SelectKBest(mutual_info_classif, k=k)
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elif ml_type[1] == "multi":
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selector = SelectKBest(f_classif, k=k)
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else:
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raise ValueError("Unknown ML type: cannot recognize ML classification subtype.")
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elif ml_type[0] == "regression":
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selector = SelectKBest(f_regression, k=k)
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else:
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raise ValueError("Unknown ML type: cannot recognize ML type. Must be either classification or regression.")
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selector.fit(self.X, self.y)
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cols_idxs = selector.get_support(indices=True)
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self.X = self.X.iloc[:,cols_idxs]
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print(self.X.columns)
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# Sequential feature addition / removal
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n_features = self.X.shape[1]
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if n_min > n_features:
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raise ValueError("The number of remaining 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 = self.X.columns.tolist()
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feature_importance = []
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if method == "remove":
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best_score = 0
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best_feature_indx = None
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i_worse = 0
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for i in reversed(range(n_features)):
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print("Iteration:", i+1)
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if i+1 == n_min:
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break
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best_feature, best_metric_score, best_metric_score_std = \
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self.select_best_feature(features, method=method, ml_type=ml_type[0], metric=metric)
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feature_importance.append((i+1, best_feature, best_metric_score, best_metric_score_std))
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features.remove(best_feature)
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if i <= n_max:
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if best_metric_score >= best_score:
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best_score = best_metric_score
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best_feature_indx = i+1
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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_tolerance:
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break
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feature_importance_df = pd.DataFrame(feature_importance, columns=['i', 'name', 'metric', 'metric_sd'])
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print(feature_importance_df)
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print("best_feature_indx", best_feature_indx)
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print("best_score", best_score)
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features_to_remove = feature_importance_df[feature_importance_df["i"] >= best_feature_indx]["name"].values.tolist()
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selected_features = [feat for feat in self.X.columns.tolist() if feat not in features_to_remove]
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return selected_features
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"""
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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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print(selection_area)
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diffrences = selection_area.diff()
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diffrences.dropna(how='any', inplace=True)
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print(diffrences)
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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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print(cumulative_sumation)
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tipping_feature_indx_1 = cumulative_sumation.idxmax()["metric"]
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print(tipping_feature_indx_1)
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# Metoda, ki pusti n_tolerance značilkam, da premagajo dosedajno najboljši score
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tipping_feature_indx_2 = None
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best_score = 0
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i_worse = 0
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for indx, row in selection_area.iterrows():
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if row["metric"] > best_score:
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tipping_feature_indx_2 = indx
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best_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_tolerance:
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break
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print(tipping_feature_indx_2)
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selection_area.reset_index(inplace=True)
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features_to_remove = feature_importance_df[feature_importance_df["i"] >= tipping_feature_indx_2[0]]["name"].values.tolist()
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selected_features = [feat for feat in self.X.columns.tolist() if feat not in features_to_remove]
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"""
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else:
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raise ValueError("Method type not recognized: only the 'remove' method is currently implemented.") |