Implement feature selection method which is used in ML pipeline.
Primoz
parent
10ca47583c
commit
ce13a9e13b
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@ -26,24 +26,45 @@ if nb_dir not in sys.path:
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from machine_learning.cross_validation import CrossValidation
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from machine_learning.preprocessing import Preprocessing
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from machine_learning.feature_selection import FeatureSelection
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# %%
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df = pd.read_csv("../data/stressfulness_event_with_speech/input_appraisal_stressfulness_event_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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df.set_index(index_columns, inplace=True)
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# Create binary target
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bins = [-1, 0, 4] # bins for stressfulness (0-4) target
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df['target'], edges = pd.cut(df.target, bins=bins, labels=[0, 1], retbins=True, right=True) #['low', 'medium', 'high']
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nan_cols = df.columns[df.isna().any()].tolist()
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df[nan_cols] = df[nan_cols].fillna(round(df[nan_cols].median(), 0))
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cv = CrossValidation(data=df, cv_method="logo")
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categorical_columns = ["gender", "startlanguage", "mostcommonactivity", "homelabel"]
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interval_feature_list, other_feature_list = [], []
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print(df.columns.tolist())
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# %%
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for split in cv.get_splits():
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train_X, train_y, test_X, test_y = cv.get_train_test_sets(split)
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pre = Preprocessing(train_X, train_y, test_X, test_y)
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pre.one_hot_encode_train_and_test_sets(categorical_columns)
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train_X, train_y, test_X, test_y = pre.get_train_test_sets()
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# train_X = train_X[train_X.columns[:30]]
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# Feature selection on train set
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# Morda se implementira GroupKfold namesto stratifiedKFold? >>
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# >> Tako se bo posamezen pid pojavil ali v test ali v train setu
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fs = FeatureSelection(train_X, train_y)
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selected_features = fs.select_features(n_min=20, n_max=60, n_not_improve=3)
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print(selected_features)
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print(len(selected_features))
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break
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# %%
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@ -1,11 +1,13 @@
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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 SequentialFeatureSelector
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from sklearn.model_selection import cross_validate, StratifiedKFold
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from sklearn.naive_bayes import GaussianNB
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from sklearn.linear_model import Lasso
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@ -21,11 +23,12 @@ from sklearn.linear_model import Lasso
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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 # TODO....
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def __init__(self, X, y):
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self.X = X
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self.y = y
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def select_best_feature(df, features, method="remove", ml_type="classification", metric="recall", stored_features=[]):
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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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@ -56,18 +59,18 @@ class FeatureSelection:
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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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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, y = df.drop(columns=['target', 'pid'])[pred_features], df['target']
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X = self.X[pred_features].copy()
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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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y=self.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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@ -137,85 +140,114 @@ class FeatureSelection:
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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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def select_features(self, n_min=20, n_max=50, method="remove", n_not_improve=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]. The best score is detected using a removal procedure.
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The procedure sequentially removes the features that attribute the least to the choosen evaluation metric.
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If in this sequence the score ML score is improved the next feature is remove otherwise there is a
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tolerance criteria (n_not_improve) with which the next n remove features are inspected whether
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currently best score is improved. The features are returned in specified interval as a list.
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Args:
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n_min (int): Minimal amount of features returned.
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n_max (int): Maximal amount of features returned.
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method (str, optional): "remove" or "add" features. Defaults to "remove".
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n_not_improve (int): 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 = 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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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 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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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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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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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(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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self.select_best_feature(features, method=method, ml_type="classification", metric="recall")
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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_not_improve:
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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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# 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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tipping_feature_indx_2 = None
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best_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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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_not_improve:
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break
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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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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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pass
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def execute_feature_selection_step():
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pass
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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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