Scikit-learn Pipelines
import pandas as pd import numpy as np import joblib from sklearn.model_selection import train_test_split from sklearn.impute import SimpleImputer from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.pipeline import make_pipeline, Pipeline from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.compose import ColumnTransformer d1 = {'Social_media_followers':[1000000, np.nan, 2000000, 1310000, 1700000, np.nan, 4100000, 1600000, 2200000, 1000000], 'Sold_out':[1,0,0,1,0,0,0,1,0,1]} df1 = pd.DataFrame(data=d1) X1 = df1[['Social_media_followers']] y1 = df1[['Sold_out']] X1_train, X1_test, y1_train, y1_test = train_test_split(X1,y1,test_size=0.3,random_state=19) imputer = SimpleImputer(strategy='mean') lr = LogisticRegression() pipe1 = make_pipeline(imputer, lr) pipe1.fit(X1_train, y1_train) pipe1.score(X1_train,y1_train) pipe1.score(X1_test,y1_test) pipe1.named_steps.simpleimputer.statistics_ pipe1.named_steps.logisticregression.coef_ Pipelines for numerical and categorical data must be separate
d2 = {'Genre':['Rock', 'Metal', 'Bluegrass', 'Rock', np.nan, 'Rock', 'Rock', np.nan, 'Bluegrass', 'Rock'], 'Social_media_followers':[1000000, np.nan, 2000000, 1310000, 1700000, np.nan, 4100000, 1600000, 2200000, 1000000], 'Sold_out':[1,0,0,1,0,0,0,1,0,1]} df = pd.DataFrame(data=d2) df.head(10) X = df.iloc[:, 0:2] y = df.iloc[:,2] X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=75) num_cols = ['Social_media_followers'] cat_cols = ['Genre'] num_pipeline = Pipeline(steps=[ ('impute', SimpleImputer(strategy='mean')), ('scale',StandardScaler()) ]) cat_pipeline = Pipeline(steps=[ ('impute', SimpleImputer(strategy='most_frequent')), ('one-hot',OneHotEncoder(handle_unknown='ignore', sparse_output=False)) ]) col_trans = ColumnTransformer(transformers=[ ('num_pipeline',num_pipeline,num_cols), ('cat_pipeline',cat_pipeline,cat_cols) ], remainder='drop', n_jobs=-1) dtc = DecisionTreeClassifier() pipefinal = make_pipeline(col_trans, dtc) pipefinal.fit(X_train, y_train) pipefinal.score(X_test, y_test) Save pipeline
joblib.dump(pipefinal,"pipe.joblib") pipefinal2 = joblib.load("pipe.joblib") 