[PYTHON] Pipeline-Vorlage für DataLiner vorverarbeiten

Einführung

Mendokusai, um die Verarbeitung hinzuzufügen, während Sie die Referenz einzeln betrachten! Es ist eine Vorlage für alle, die sagen. Bitte kommentieren oder ersetzen Sie es entsprechend Ihren Anforderungen.

Installation

! pip install -U dataliner

Vorlage

import dataliner as dl
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import make_pipeline

X, X_test, y = dl.load_titanic()

process = make_pipeline(
    dl.DropColumns(drop_columns=['PassengerId']),
    dl.DropNoVariance(),
    dl.GroupRareCategory(threshold=0.01),
    dl.DropHighCardinality(max_categories=50),
    dl.DropLowAUC(threshold=0.51),
    dl.ClipData(threshold=0.99),
    dl.BinarizeNaN(),
    dl.CountRowNaN(),
    dl.ImputeNaN(cat_strategy='mode', num_strategy='mean'),
    dl.AppendEncoder(dl.TargetMeanEncoding(k=0, f=1, smoothing=True)),
    dl.OneHotEncoding(drop_first=True),
#     dl.TargetMeanEncoding(k=0, f=1, smoothing=True),
#     dl.CountEncoding(),
#     dl.RankedCountEncoding(),
#     dl.FrequencyEncoding(),
#     dl.RankedTargetMeanEncoding(k=0, f=1, smoothing=True),
#     dl.RankedEvaluationMetricEncoding(metric='roc_auc'),
    dl.StandardScaling(),
#     dl.MinMaxScaling(),
    dl.UnionAppend([
        dl.AppendCluster(n_clusters=8, random_state=1234),
        dl.AppendAnomalyScore(n_estimators=100, random_state=1234),
        dl.AppendPrincipalComponent(n_components=5, random_state=1234),
        dl.AppendClusterTargetMean(n_clusters=8, random_state=1234),
        dl.AppendClusterDistance(n_clusters=8, random_state=1234),
        dl.AppendArithmeticFeatures(max_features=50, metric='roc_auc', operation='add', replace_zero=0.001),
        dl.AppendArithmeticFeatures(max_features=50, metric='roc_auc', operation='subtract', replace_zero=0.001),
        dl.AppendArithmeticFeatures(max_features=50, metric='roc_auc', operation='multiply', replace_zero=0.001),
        dl.AppendArithmeticFeatures(max_features=50, metric='roc_auc', operation='divide', replace_zero=0.001),
#        dl.AppendClassificationModel(model=RandomForestClassifier(), probability=False)
    ]),
#     dl.DropLowAUC(),
    dl.PermutationImportanceTest(threshold=0.0001),
    dl.DropHighCorrelation(threshold=0.95),
)

Xt = process.fit_transform(X, y)
Xt_test = process.transform(X)