FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches
https://doi.org/10.1109/TVCG.2022.3141040
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98 lines
3.3 KiB
98 lines
3.3 KiB
# first line: 868
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@memory.cache
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def estimatorFeatureSelection(Data, clf):
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resultsFS = []
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permList = []
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PerFeatureAccuracy = []
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PerFeatureAccuracyAll = []
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ImpurityFS = []
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RankingFS = []
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estim = clf.fit(Data, yData)
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importances = clf.feature_importances_
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# std = np.std([tree.feature_importances_ for tree in estim.feature_importances_],
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# axis=0)
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maxList = max(importances)
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minList = min(importances)
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for f in range(Data.shape[1]):
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ImpurityFS.append((importances[f] - minList) / (maxList - minList))
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estim = LogisticRegression(n_jobs = -1, random_state=RANDOM_SEED)
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selector = RFECV(estimator=estim, n_jobs = -1, step=1, cv=crossValidation)
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selector = selector.fit(Data, yData)
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RFEImp = selector.ranking_
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for f in range(Data.shape[1]):
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if (RFEImp[f] == 1):
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RankingFS.append(0.95)
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elif (RFEImp[f] == 2):
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RankingFS.append(0.85)
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elif (RFEImp[f] == 3):
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RankingFS.append(0.75)
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elif (RFEImp[f] == 4):
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RankingFS.append(0.65)
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elif (RFEImp[f] == 5):
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RankingFS.append(0.55)
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elif (RFEImp[f] == 6):
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RankingFS.append(0.45)
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elif (RFEImp[f] == 7):
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RankingFS.append(0.35)
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elif (RFEImp[f] == 8):
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RankingFS.append(0.25)
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elif (RFEImp[f] == 9):
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RankingFS.append(0.15)
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else:
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RankingFS.append(0.05)
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perm = PermutationImportance(clf, cv=None, refit = True, n_iter = 25).fit(Data, yData)
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permList.append(perm.feature_importances_)
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n_feats = Data.shape[1]
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num_cores = multiprocessing.cpu_count()
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print("Parallelization Initialized")
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flat_results = Parallel(n_jobs=num_cores)(delayed(featFun)(clf,Data.values[:, i].reshape(-1, 1),yData) for i in range(n_feats))
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PerFeatureAccuracy = [item for sublist in flat_results for item in sublist]
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# for i in range(n_feats):
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# scoresHere = model_selection.cross_val_score(clf, Data.values[:, i].reshape(-1, 1), yData, cv=None, n_jobs=-1)
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# PerFeatureAccuracy.append(scoresHere.mean())
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PerFeatureAccuracyAll.append(PerFeatureAccuracy)
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clf.fit(Data, yData)
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yPredict = clf.predict(Data)
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yPredict = np.nan_to_num(yPredict)
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RankingFSDF = pd.DataFrame(RankingFS)
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RankingFSDF = RankingFSDF.to_json()
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ImpurityFSDF = pd.DataFrame(ImpurityFS)
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ImpurityFSDF = ImpurityFSDF.to_json()
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perm_imp_eli5PD = pd.DataFrame(permList)
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if (perm_imp_eli5PD.empty):
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for col in Data.columns:
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perm_imp_eli5PD.append({0:0})
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perm_imp_eli5PD = perm_imp_eli5PD.to_json()
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PerFeatureAccuracyPandas = pd.DataFrame(PerFeatureAccuracyAll)
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PerFeatureAccuracyPandas = PerFeatureAccuracyPandas.to_json()
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bestfeatures = SelectKBest(score_func=f_classif, k='all')
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fit = bestfeatures.fit(Data,yData)
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dfscores = pd.DataFrame(fit.scores_)
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dfcolumns = pd.DataFrame(Data.columns)
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featureScores = pd.concat([dfcolumns,dfscores],axis=1)
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featureScores.columns = ['Specs','Score'] #naming the dataframe columns
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featureScores = featureScores.to_json()
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resultsFS.append(featureScores)
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resultsFS.append(ImpurityFSDF)
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resultsFS.append(perm_imp_eli5PD)
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resultsFS.append(PerFeatureAccuracyPandas)
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resultsFS.append(RankingFSDF)
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return resultsFS
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