#!/usr/bin/env python # coding: utf-8 from sklearn.datasets import load_iris from sklearn.preprocessing import MinMaxScaler from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn2pmml import PMMLPipeline iris = load_iris() X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size = 0.25) y_train pipe_l = PMMLPipeline([('minmax', MinMaxScaler()), ('lr', LogisticRegression())]) model = pipe_l.fit(X_train,y_train) pipe_l.score(X_test, y_test) X_test[1] y_test[1] pipe_l.predict([[6.7, 3. , 5.2, 2.3]]) import pandas as pd irisd = pd.DataFrame(iris.data, columns=iris.feature_names) irisd.columns from nyoka import skl_to_pmml skl_to_pmml(pipe_l, irisd.columns, 'Target', "iris_nyoka_pipeline.pmml") from sklearn2pmml import sklearn2pmml sklearn2pmml(pipe_l, "iris_pipeline.pmml", with_repr = True, debug = True)
# nn nn = PMMLPipeline([("classifier", sk_nn.MLPClassifier(activation='tanh', solver='adam', alpha=0.0001, learning_rate='adaptive', learning_rate_init=0.001, max_iter=1000))]) # 模型拟合 nn.fit(data, label) # 模型预测 nn_predict = nn.predict(X_test) # 模型评估 # 基础打分 nn_score = nn.score(X_test, y_test) print(nn_score) # 交叉验证 nn_cross1 = cross_val_score(nn, X_train, y_train, scoring='accuracy', cv=10, n_jobs=-1) nn_cross2 = cross_val_score(nn, X_test, y_test, scoring='accuracy', cv=10, n_jobs=-1) print(nn_cross1)