#!/usr/bin/python # coding=utf8 from collections import defaultdict from operator import itemgetter from pprint import pprint from sklearn.datasets import load_iris from sklearn.metrics import classification_report from sklearn.model_selection import train_test_split __author__ = 'Jam' __date__ = '2019/6/6 14:54' import numpy as np """ 亲和性分析 如果一个顾客买了商品X，那么他们可能愿意买商品Y 衡量方法： 支持度support := 所有买X的人数 置信度confidence := 所有买𝑋和𝑌的人数/所有买𝑋的人数 """ def crate_random_data(): X = np.zeros((100, 5), dtype='bool') for i in range(X.shape[0]): if np.random.random() < 0.3: X[i][0] = 1 if np.random.random() < 0.5: X[i][1] = 1 if np.random.random() < 0.2: X[i][2] = 1 if np.random.random() < 0.25: X[i][3] = 1 if np.random.random() < 0.5: X[i][4] = 1 else: if np.random.random() < 0.5: X[i][1] = 1 if np.random.random() < 0.2: X[i][2] = 1 if np.random.random() < 0.25: X[i][3] = 1 if np.random.random() < 0.5: X[i][4] = 1 else: if np.random.random() < 0.8: X[i][2] = 1 if np.random.random() < 0.6: X[i][3] = 1 if np.random.random() < 0.7: X[i][4] = 1 if X[i].sum() == 0: X[i][4] = 1 np.savetxt("./data/affinity_dataset.txt", X, fmt='%d') # 保存 def print_rule(premise, conclusion, support, confidence, features): premise_name = features[premise] conclusion_name = features[conclusion] print("Rule: 买了{0}，又买{1}".format(premise_name, conclusion_name)) print(" - 置信度Confidence: {0:.3f}".format(confidence[(premise, conclusion)])) print(" - 支持度Support: {0}".format(support[(premise, conclusion)])) print("") def special_condiction(): dataset_filename = "./data/affinity_dataset.txt" X = np.loadtxt(dataset_filename) # 加载数据 # features = ["bread", "milk", "cheese", "apples", "bananas"] num_apple_purchases = 0 # 计数 for sample in X: if sample[3] == 1: # 记录买 Apples　的有多少人 num_apple_purchases += 1 print("买苹果的有{0}人".format(num_apple_purchases)) rule_valid = 0 rule_invalid = 0 for sample in X: if sample[3] == 1: # 买了苹果 if sample[4] == 1: # 又买香蕉的 rule_valid += 1 else: # 不买香蕉的 rule_invalid += 1 print("买了苹果又买香蕉的有{0}人".format(rule_valid)) print("买了苹果不买香蕉的有{0}人".format(rule_invalid)) support = rule_valid confidence = rule_valid * 1.0 / num_apple_purchases print("支持度support = {0} 置信度confidence = {1:.3f}.".format(support, confidence)) print("置信度confidence的百分比形式为 {0:.1f}%.".format(100 * confidence)) def every_condiction(): dataset_filename = "./data/affinity_dataset.txt" X = np.loadtxt(dataset_filename) n_samples, n_features = X.shape features = ["bread", "milk", "cheese", "apples", "bananas"] valid_rules = defaultdict(int) invalid_rules = defaultdict(int) num_occurences = defaultdict(int) for sample in X: for premise in range(n_features): if sample[premise] == 0: continue num_occurences[premise] += 1 for conclusion in range(n_features): if premise == conclusion: continue if sample[conclusion] == 1: valid_rules[(premise, conclusion)] += 1 else: invalid_rules[(premise, conclusion)] += 1 support = valid_rules confidence = defaultdict(float) for premise, conclusion in valid_rules.keys(): confidence[(premise, conclusion)] = valid_rules[(premise, conclusion)] * 1.0 / num_occurences[premise] pprint(list(support.items())) pprint(list(confidence.items())) sorted_confidence = sorted(confidence.items(), key=itemgetter(1), reverse=True) pprint(sorted_confidence) for index in range(5): print("Rule #{0}".format(index + 1)) premise, conclusion = sorted_confidence[index][0] print_rule(premise, conclusion, support, confidence, features) """ 给出某一植物部分特征，预测该植物是什么 特征： 萼片长宽sepal width, sepal height 花瓣长宽petal width, petal height 算法： For each variable For each value of the variable The prediction based on this variable goes the most frequent class Compute the error of this prediction Sum the prediction errors for all values of the variable Use the variable with the lowest error """ def analysis_iris_data(): dataset = load_iris() X = dataset.data y = dataset.target n_samples, n_features = X.shape print n_samples, n_features attribute_means = X.mean(axis=0) assert attribute_means.shape == (n_features,) X_d = np.array(X >= attribute_means, dtype='int') random_state = 14 X_train, X_test, y_train, y_test = train_test_split(X_d, y, random_state=random_state) print("训练集数据有 {} 条".format(y_train.shape)) print("测试集数据有 {} 条".format(y_test.shape)) all_predictors = {variable: train(X_train, y_train, variable) for variable in range(X_train.shape[1])} errors = {variable: error for variable, (mapping, error) in all_predictors.items()} best_variable, best_error = sorted(errors.items(), key=itemgetter(1))[0] print("The best model is based on variable {0} and has error {1:.2f}".format(best_variable, best_error)) model = { 'variable': best_variable, 'predictor': all_predictors[best_variable][0] } print(model) y_predicted = predict(X_test, model) print(y_predicted) accuracy = np.mean(y_predicted == y_test) * 100 print("在测试集上的准确率 {:.1f}%".format(accuracy)) print(classification_report(y_test, y_predicted)) def train(X, y_true, feature): n_samples, n_features = X.shape assert 0 <= feature < n_features values = set(X[:, feature]) predictors = dict() errors = [] for current_value in values: most_frequent_class, error = train_feature_value(X, y_true, feature, current_value) predictors[current_value] = most_frequent_class errors.append(error) total_error = sum(errors) return predictors, total_error # Compute what our predictors say each sample is based on its value # y_predicted = np.array([predictors[sample[feature]] for sample in X]) def train_feature_value(X, y_true, feature, value): class_counts = defaultdict(int) for sample, y in zip(X, y_true): if sample[feature] == value: class_counts[y] += 1 sorted_class_counts = sorted(class_counts.items(), key=itemgetter(1), reverse=True) most_frequent_class = sorted_class_counts[0][0] n_samples = X.shape[1] error = sum([class_count for class_value, class_count in class_counts.items() if class_value != most_frequent_class]) return most_frequent_class, error def predict(X_test, model): variable = model['variable'] predictor = model['predictor'] y_predicted = np.array([predictor[int(sample[variable])] for sample in X_test]) return y_predicted def main(): analysis_iris_data() if __name__ == '__main__': main()