feat: sync all remaining python source board configurations
This commit is contained in:
yczpf2019
264
mixly/boards/default_src/python_mixpy/generators/sklearn.js
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264
mixly/boards/default_src/python_mixpy/generators/sklearn.js
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export const sklearn_make_classification = function (_, generator) {
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var value_n_samples = generator.valueToCode(this, 'n_samples', generator.ORDER_ATOMIC) || '100';
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var value_n_features = generator.valueToCode(this, 'n_features', generator.ORDER_ATOMIC) || '20';
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var value_n_informative = generator.valueToCode(this, 'n_informative', generator.ORDER_ATOMIC) || '2';
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var value_n_redundant = generator.valueToCode(this, 'n_redundant', generator.ORDER_ATOMIC) || '2';
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var value_n_repeated = generator.valueToCode(this, 'n_repeated', generator.ORDER_ATOMIC) || '0';
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var value_n_classes = generator.valueToCode(this, 'n_classes', generator.ORDER_ATOMIC) || '2';
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var value_n_clusters_per_class = generator.valueToCode(this, 'n_clusters_per_class', generator.ORDER_ATOMIC) || '2';
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var value_random_state = generator.valueToCode(this, 'random_state', generator.ORDER_ATOMIC) || 'None';
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generator.definitions_['import_sklearn_make_classification'] = 'from sklearn.datasets import make_classification';
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var code = 'make_classification(n_samples=' + value_n_samples + ', n_features=' + value_n_features + ', n_informative=' + value_n_informative + ', n_redundant=' + value_n_redundant + ', n_repeated=' + value_n_repeated + ', n_classes=' + value_n_classes + ', n_clusters_per_class=' + value_n_clusters_per_class + ', random_state=' + value_random_state + ')';
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return [code, generator.ORDER_ATOMIC];
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}
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// sklearn 生成回归样本
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export const sklearn_make_regression = function (_, generator) {
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var value_n_samples = generator.valueToCode(this, 'n_samples', generator.ORDER_ATOMIC) || '100';
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var value_n_features = generator.valueToCode(this, 'n_features', generator.ORDER_ATOMIC) || '100';
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var value_n_informative = generator.valueToCode(this, 'n_informative', generator.ORDER_ATOMIC) || '10';
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var value_n_targets = generator.valueToCode(this, 'n_targets', generator.ORDER_ATOMIC) || '1';
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var value_bias = generator.valueToCode(this, 'bias', generator.ORDER_ATOMIC) || '0.0';
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var value_noise = generator.valueToCode(this, 'noise', generator.ORDER_ATOMIC) || '0.0';
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var value_random_state = generator.valueToCode(this, 'random_state', generator.ORDER_ATOMIC) || 'None';
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generator.definitions_['import_sklearn_make_regression'] = 'from sklearn.datasets import make_regression';
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var code = 'make_regression(n_samples=' + value_n_samples + ', n_features=' + value_n_features + ', n_informative=' + value_n_informative + ', n_targets=' + value_n_targets + ', bias=' + value_bias + ', noise=' + value_noise + ', random_state=' + value_random_state + ')';
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return [code, generator.ORDER_ATOMIC];
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}
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// sklearn 生成聚类样本
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export const sklearn_make_blobs = function (_, generator) {
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var value_n_samples = generator.valueToCode(this, 'n_samples', generator.ORDER_ATOMIC) || '100';
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var value_n_features = generator.valueToCode(this, 'n_features', generator.ORDER_ATOMIC) || '2';
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var value_centers = generator.valueToCode(this, 'centers', generator.ORDER_ATOMIC) || 'None';
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var value_cluster_std = generator.valueToCode(this, 'cluster_std', generator.ORDER_ATOMIC) || '1.0';
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var value_center_box = generator.valueToCode(this, 'center_box', generator.ORDER_ATOMIC) || '(-10.0,10.0)';
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var value_shuffle = generator.valueToCode(this, 'shuffle', generator.ORDER_ATOMIC) || 'True';
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var value_random_state = generator.valueToCode(this, 'random_state', generator.ORDER_ATOMIC) || 'None';
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generator.definitions_['import_sklearn_make_blobs'] = 'from sklearn.datasets import make_blobs';
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var code = 'make_blobs(n_samples=' + value_n_samples + ', n_features=' + value_n_features + ', centers=' + value_centers + ', cluster_std=' + value_cluster_std + ', center_box=' + value_center_box + ', shuffle=' + value_shuffle + ', random_state=' + value_random_state + ')';
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return [code, generator.ORDER_ATOMIC];
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}
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// sklearn 加载数据集
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export const sklearn_load = function (_, generator) {
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var dropdown_type = this.getFieldValue('type');
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var text_name = this.getFieldValue('name');
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generator.definitions_['import_sklearn_datasets'] = 'from sklearn import datasets';
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var code = text_name + ' = datasets.' + dropdown_type + '()\n';
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return code;
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}
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// sklearn 获取特征值/标签值/标签/特征
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export const sklearn_data_target = function (_, generator) {
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var value_name = generator.valueToCode(this, 'name', generator.ORDER_ATOMIC) || 'iris';
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var dropdown_type = this.getFieldValue('type');
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var code = value_name + '.' + dropdown_type;
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return [code, generator.ORDER_ATOMIC];
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}
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// sklearn 数据集分割
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export const sklearn_train_test_split = function (_, generator) {
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var value_train_data = generator.valueToCode(this, 'train_data', generator.ORDER_ATOMIC) || 'iris_X';
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var value_train_target = generator.valueToCode(this, 'train_target', generator.ORDER_ATOMIC) || 'iris_y';
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var value_test_size = generator.valueToCode(this, 'test_size', generator.ORDER_ATOMIC) || '0.3';
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var value_rondom_state = generator.valueToCode(this, 'rondom_state', generator.ORDER_ATOMIC) || 'None';
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generator.definitions_['import_sklearn_train_test_split'] = 'from sklearn.model_selection import train_test_split';
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if (value_train_target == 'None') {
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var code = 'train_test_split(' + value_train_data + ', test_size=' + value_test_size + ', random_state=' + value_rondom_state + ')';
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} else {
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var code = 'train_test_split(' + value_train_data + ', ' + value_train_target + ', test_size=' + value_test_size + ', random_state=' + value_rondom_state + ')';
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}
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return [code, generator.ORDER_ATOMIC];
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}
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// sklearn 数据集分割-无标签值
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export const sklearn_train_test_split_no_target = function (_, generator) {
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var value_train_data = generator.valueToCode(this, 'train_data', generator.ORDER_ATOMIC) || 'iris_X';
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var value_test_size = generator.valueToCode(this, 'test_size', generator.ORDER_ATOMIC) || '0.3';
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var value_rondom_state = generator.valueToCode(this, 'rondom_state', generator.ORDER_ATOMIC) || 'None';
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generator.definitions_['import_sklearn_train_test_split'] = 'from sklearn.model_selection import train_test_split';
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var code = 'train_test_split(' + value_train_data + ', test_size=' + value_test_size + ', random_state=' + value_rondom_state + ')';
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return [code, generator.ORDER_ATOMIC];
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}
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// sklearn 初始化线性回归
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export const sklearn_LinearRegression = function (_, generator) {
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var value_model_name = generator.valueToCode(this, 'model_name', generator.ORDER_ATOMIC) || 'model';
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var value_fit_intercept = generator.valueToCode(this, 'fit_intercept', generator.ORDER_ATOMIC) || 'True';
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var value_normalize = generator.valueToCode(this, 'normalize', generator.ORDER_ATOMIC) || 'False';
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var value_n_jobs = generator.valueToCode(this, 'n_jobs', generator.ORDER_ATOMIC) || 'None';
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generator.definitions_['import_sklearn_linear_model'] = 'from sklearn.linear_model import LinearRegression';
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var code = value_model_name + ' = LinearRegression(fit_intercept=' + value_fit_intercept + ', normalize=' + value_normalize + ', n_jobs=' + value_n_jobs + ')\n';
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return code;
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}
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// sklearn 初始化岭回归
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export const sklearn_Ridge = function (_, generator) {
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var value_model_name = generator.valueToCode(this, 'model_name', generator.ORDER_ATOMIC) || 'model';
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var value_alpha = generator.valueToCode(this, 'alpha', generator.ORDER_ATOMIC) || '1.0';
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var value_fit_intercept = generator.valueToCode(this, 'fit_intercept', generator.ORDER_ATOMIC) || 'True';
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var value_normalize = generator.valueToCode(this, 'normalize', generator.ORDER_ATOMIC) || 'False';
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var value_max_iter = generator.valueToCode(this, 'max_iter', generator.ORDER_ATOMIC) || '300';
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var value_random_state = generator.valueToCode(this, 'random_state', generator.ORDER_ATOMIC) || 'None';
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generator.definitions_['import_sklearn_linear_model'] = 'from sklearn.linear_model import Ridge';
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var code = value_model_name + ' = Ridge(alpha=' + value_alpha + ', fit_intercept=' + value_fit_intercept + ', normalize=' + value_normalize + ', max_iter=' + value_max_iter + ', random_state=' + value_random_state + ')\n';
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return code;
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}
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// sklearn 初始化决策树 分类/回归算法
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export const sklearn_DecisionTreeClassifier_Regressor = function (_, generator) {
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var value_model_name = generator.valueToCode(this, 'model_name', generator.ORDER_ATOMIC) || 'model';
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var dropdown_type = this.getFieldValue('type');
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var value_max_depth = generator.valueToCode(this, 'max_depth', generator.ORDER_ATOMIC) || 'None';
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var value_random_state = generator.valueToCode(this, 'random_state', generator.ORDER_ATOMIC) || 'None';
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generator.definitions_['import_sklearn_' + dropdown_type] = 'from sklearn.tree import ' + dropdown_type;
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var code = value_model_name + ' = ' + dropdown_type + '(max_depth=' + value_max_depth + ', random_state=' + value_random_state + ')\n';
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return code;
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}
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export const sklearn_RandomForestClassifier_Regressor = function (_, generator) {
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var dropdown_type = this.getFieldValue('type');
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var value_model_name = generator.valueToCode(this, 'model_name', generator.ORDER_ATOMIC) || 'Model';
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var value_n_estimators = generator.valueToCode(this, 'n_estimators', generator.ORDER_ATOMIC) || '100';
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var value_max_depth = generator.valueToCode(this, 'max_depth', generator.ORDER_ATOMIC) || 'None';
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var value_n_jobs = generator.valueToCode(this, 'n_jobs', generator.ORDER_ATOMIC) || 'None';
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var value_random_state = generator.valueToCode(this, 'random_state', generator.ORDER_ATOMIC) || 'None';
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generator.definitions_['import_sklearn_' + dropdown_type] = 'from sklearn.ensemble import ' + dropdown_type;
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var code = value_model_name + ' = ' + dropdown_type + '(n_estimators=' + value_n_estimators + ', max_depth=' + value_max_depth + ', n_jobs=' + value_n_jobs + ', random_state=' + value_random_state + ')\n';
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return code;
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}
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// sklearn 初始化K近邻 分类/回归算法
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export const sklearn_KNeighborsClassifier_Regressor = function (_, generator) {
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var dropdown_type = this.getFieldValue('type');
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var value_model_name = generator.valueToCode(this, 'model_name', generator.ORDER_ATOMIC) || 'model';
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var value_K = generator.valueToCode(this, 'K', generator.ORDER_ATOMIC) || '5';
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var value_n_jobs = generator.valueToCode(this, 'n_jobs', generator.ORDER_ATOMIC) || 'None';
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generator.definitions_['import_sklearn_' + dropdown_type] = 'from sklearn.neighbors import ' + dropdown_type;
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var code = value_model_name + ' = ' + dropdown_type + '(n_neighbors=' + value_K + ', n_jobs=' + value_n_jobs + ')\n';
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return code;
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}
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// sklearn 初始化高斯贝叶斯分类算法
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export const sklearn_GaussianNB = function (_, generator) {
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var value_model_name = generator.valueToCode(this, 'model_name', generator.ORDER_ATOMIC) || 'model';
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generator.definitions_['import_sklearn_GaussianNB'] = 'from sklearn.naive_bayes import GaussianNB';
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var code = value_model_name + ' = GaussianNB()\n';
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return code;
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}
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// sklearn 初始化PCA降维
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export const sklearn_pca = function (_, generator) {
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var value_model_name = generator.valueToCode(this, 'model_name', generator.ORDER_ATOMIC) || 'pca';
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var value_n_components = generator.valueToCode(this, 'n_components', generator.ORDER_ATOMIC) || '2';
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generator.definitions_['import_sklearn_pca'] = 'from sklearn.decomposition import PCA';
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var code = value_model_name + ' = PCA(n_components=' + value_n_components + ')\n';
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return code;
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}
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export const sklearn_pca_fit_transform = function(block, generator) {
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var value_model_name = generator.valueToCode(block, 'model_name', generator.ORDER_ATOMIC);
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var value_train_data = generator.valueToCode(block, 'train_data', generator.ORDER_ATOMIC);
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var code = value_model_name + '.fit_transform(' + value_train_data + ')';
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return [code, generator.ORDER_ATOMIC];
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};
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// sklearn 初始K-均值聚类
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export const sklearn_KMeans = function (_, generator) {
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var value_model_name = generator.valueToCode(this, 'model_name', generator.ORDER_ATOMIC) || 'model';
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var value_n_clusters = generator.valueToCode(this, 'n_clusters', generator.ORDER_ATOMIC) || '8';
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var value_max_iter = generator.valueToCode(this, 'max_iter', generator.ORDER_ATOMIC) || '300';
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var value_random_state = generator.valueToCode(this, 'random_state', generator.ORDER_ATOMIC) || 'None';
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generator.definitions_['import_sklearn_KMeans'] = 'from sklearn.cluster import KMeans';
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var code = value_model_name + ' = KMeans(n_clusters=' + value_n_clusters + ', max_iter=' + value_max_iter + ', random_state=' + value_random_state + ')\n';
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return code;
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}
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export const sklearn_KMeans_fit = function(block, generator) {
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var value_model_name = generator.valueToCode(block, 'model_name', generator.ORDER_ATOMIC);
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var value_train_data = generator.valueToCode(block, 'train_data', generator.ORDER_ATOMIC);
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var code = value_model_name + '.fit(' + value_train_data + ')\n';
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return code;
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};
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// sklearn 训练模型
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export const sklearn_fit = function (_, generator) {
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var value_model_name = generator.valueToCode(this, 'model_name', generator.ORDER_ATOMIC) || 'model';
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var value_train_data = generator.valueToCode(this, 'train_data', generator.ORDER_ATOMIC) || 'X_train';
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var value_train_target = generator.valueToCode(this, 'train_target', generator.ORDER_ATOMIC) || 'y_train';
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if (value_train_target == 'None') {
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var code = value_model_name + '.fit(' + value_train_data + ')\n';
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} else {
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var code = value_model_name + '.fit(' + value_train_data + ', ' + value_train_target + ')\n';
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}
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return code;
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}
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// sklearn 训练模型-无标签值
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export const sklearn_fit_no_target = function (_, generator) {
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var value_model_name = generator.valueToCode(this, 'model_name', generator.ORDER_ATOMIC) || 'model';
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var value_train_data = generator.valueToCode(this, 'train_data', generator.ORDER_ATOMIC) || 'X_train';
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var code = value_model_name + '.fit(' + value_train_data + ')\n';
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return code;
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}
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// sklearn 模型预测
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export const sklearn_predict = function (_, generator) {
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var value_model_name = generator.valueToCode(this, 'model_name', generator.ORDER_ATOMIC) || 'model';
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var value_train_data = generator.valueToCode(this, 'train_data', generator.ORDER_ATOMIC) || 'X_test';
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var code = value_model_name + '.predict(' + value_train_data + ')';
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return [code, generator.ORDER_ATOMIC];
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}
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// sklearn 计算模型得分
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export const sklearn_score = function (_, generator) {
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var value_model_name = generator.valueToCode(this, 'model_name', generator.ORDER_ATOMIC) || 'model';
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var value_train_data = generator.valueToCode(this, 'train_data', generator.ORDER_ATOMIC) || 'X_train';
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var value_train_target = generator.valueToCode(this, 'train_target', generator.ORDER_ATOMIC) || 'y_train';
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if (value_train_target == 'None') {
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var code = value_model_name + '.score(' + value_train_data + ')';
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} else {
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var code = value_model_name + '.score(' + value_train_data + ', ' + value_train_target + ')';
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}
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return [code, generator.ORDER_ATOMIC];
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}
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// sklearn 计算模型得分-无标签值
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export const sklearn_score_no_target = function (_, generator) {
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var value_model_name = generator.valueToCode(this, 'model_name', generator.ORDER_ATOMIC) || 'model';
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var value_train_data = generator.valueToCode(this, 'train_data', generator.ORDER_ATOMIC) || 'X_train';
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var code = value_model_name + '.score(' + value_train_data + ')';
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return [code, generator.ORDER_ATOMIC];
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}
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// sklearn 线性回归 模型获取 斜率/截距
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export const sklearn_coef_intercept = function (_, generator) {
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var value_model_name = generator.valueToCode(this, 'model_name', generator.ORDER_ATOMIC) || 'model';
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var dropdown_type = this.getFieldValue('type');
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var code = value_model_name + '.' + dropdown_type;
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return [code, generator.ORDER_ATOMIC];
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}
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// sklearn K-均值聚类 模型获取 簇中心坐标/聚类后的标签/所有点到对应簇中心的距离平方和
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export const sklearn_cluster_centers_labels_inertia = function (_, generator) {
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var value_model_name = generator.valueToCode(this, 'model_name', generator.ORDER_ATOMIC) || 'model';
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var dropdown_type = this.getFieldValue('type');
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var code = value_model_name + '.' + dropdown_type;
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return [code, generator.ORDER_ATOMIC];
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}
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// sklearn 保存/加载模型
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export const sklearn_save_load_model = function (_, generator) {
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var value_model_name = generator.valueToCode(this, 'model_name', generator.ORDER_ATOMIC) || 'model';
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var dropdown_type = this.getFieldValue('type');
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var value_address = generator.valueToCode(this, 'address', generator.ORDER_ATOMIC) || 'D:/mixly/test.pkl';
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generator.definitions_['import_sklearn_joblib'] = 'import joblib';
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var code = '';
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if (dropdown_type == 'dump') {
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code = 'joblib.dump(' + value_model_name + ', ' + value_address + ')\n';
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} else {
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code = value_model_name + ' = joblib.load(' + value_address + ')\n';
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}
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return code;
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}
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