def handle(self, *args, **kwargs): # get model TARGET_MODEL = 5 job = Job.objects.filter(pk=TARGET_MODEL)[0] model = joblib.load(job.predictive_model.model_path) model = model[0] training_df, test_df = get_encoded_logs(job) feature_names = list( training_df.drop(['trace_id', 'label'], 1).columns.values) X_train = training_df.drop(['trace_id', 'label'], 1) Y_train = training_df.drop( ['trace_id', 'prefix_1', 'prefix_3', 'prefix_4', 'label'], 1) rf = RuleFit() columns = list(X_train.columns) X = X_train.as_matrix() rf.fit(X, Y_train.values.ravel(), feature_names=columns) rules = rf.get_rules() # rules = rules[rules.coef != 0].sort_values("support", ascending=False) rules = rules[(rules.coef > 0.) & (rules.type != 'linear')] rules['effect'] = rules['coef'] * rules['support'] pd.set_option('display.max_colwidth', -1) rules.nlargest(10, 'effect') # print(rules) rules
def train_job(train_idx, test_idx, t_size, rf_mode, m_rules, r_seed, X, y, feas, n_samples): """ 每个 fold 中进行训练和验证 """ p = current_process() print('process counter:', p._identity[0], 'pid:', os.getpid()) # 初始化 estimator 训练集进入模型 rf = RuleFit(tree_size=t_size, rfmode=rf_mode, max_rules=m_rules, random_state=r_seed) print( "\nTree generator:{0}, \n\nMax rules:{1}, Tree size:{2}, Random state:{3}" .format(rf.tree_generator, rf.max_rules, rf.tree_size, rf.random_state)) rf.fit(X[train_idx], y[train_idx], feas) # 验证测试集 (通过 index 去除 fake data) real_test_index = test_idx[test_idx < n_samples] batch_test_x = X[real_test_index] batch_test_y = y[real_test_index] batch_test_size = len(real_test_index) y_pred = rf.predict(batch_test_x) # 计算测试集 ACC accTest = accuracy_score(batch_test_y, y_pred) print("\nTest Accuracy:", "{:.6f}".format(accTest), "Test Size:", batch_test_size) print( "\n=========================================================================" ) # 返回测试集和预测结果用于统计 return batch_test_y, y_pred
def __init__( self, mode, max_depth=3, feature_names=None, max_sentences=20000, exp_rand_tree_size=True, tree_generator=None, ): ''' mode: 'classify' or 'regress' max_depth: maximum depth of trained trees feature_names: names of features max_sentences: maximum number of extracted sentences exp_rand_tree_size: Having trees with different sizes tree_generator: Tree generator model (overwrites above features) ''' self.feature_names = feature_names self.mode = mode max_leafs = 2**max_depth num_trees = max_sentences // max_leafs if tree_generator is None: tree_generator = RandomForestClassifier(num_trees, max_depth=max_depth) self.exp_rand_tree_size = exp_rand_tree_size self.rf = RuleFit(rfmode=mode, tree_size=max_leafs, max_rules=max_sentences, tree_generator=tree_generator, exp_rand_tree_size=True, fit_lasso=False, Cs=10.**np.arange(-4, 1), cv=3)
def _getRulesRulefit(df_aux, model_params): # Prepare data X_train = df_aux[feature_cols] y_train = df_aux["predictions"] # Fit model if "tree_size" not in model_params.keys(): model_params["tree_size"] = len(feature_cols) * 2 if "rfmode" not in model_params.keys(): model_params["rfmode"] = "classify" rf = RuleFit(**model_params) rf.fit(X_train.values, y_train.values, feature_names=feature_cols) # Get rules print("Obtaining Rules using RuleFit...") rules_all = rf.get_rules() rules_all = rules_all[rules_all.coef != 0] rules_all = rules_all[rules_all.importance > 0].sort_values( "support", ascending=False) rules_all = rules_all[rules_all.coef > 0] rules_all = rules_all.sort_values("support", ascending=False) rules_all = rules_all[rules_all["type"] == "rule"] rules_all["size_rules"] = rules_all.apply( lambda x: len(x["rule"].split("&")), axis=1) # Turn list of rules to dataframe print("Turning rules to hypercubes...") df_rules = turn_rules_to_df(list_rules=list(rules_all["rule"].values), list_cols=feature_cols) # Get corresponding rule size from the original rule extraction model, # not on the hypercubes obtained later df_rules["size_rules"] = list(rules_all["size_rules"].values) # Prune rules if simplify_rules: print("Prunning the rules obtained...") df_rules_pruned = df_rules.drop(columns=["size_rules"]).copy() df_rules_pruned = simplifyRules(df_rules_pruned, categorical_cols) df_rules_pruned = df_rules_pruned.reset_index().merge( df_rules.reset_index()[["index", "size_rules"]], how="left") df_rules_pruned.index = df_rules_pruned["index"] df_rules_pruned = df_rules_pruned.drop(columns=["index"], errors="ignore") df_rules = df_rules_pruned return df_rules
def __init__(self, mode, max_depth=3, feature_names=None, max_rules=20000, exp_rand_tree_size=True, Cs=None, cv=None, tree_generator=None): super().__init__(mode, max_depth=3, feature_names=None, max_rules=20000, exp_rand_tree_size=True, Cs=None, cv=None, tree_generator=None) if Cs is None: Cs = 10.**np.arange(-4, 1) if cv is None: cv = 3 self.feature_names = feature_names self.mode = mode max_leafs = 2**max_depth num_trees = max_rules // max_leafs if tree_generator is None: tree_generator = RandomForestClassifier(num_trees, max_depth=max_depth) self.exp_rand_tree_size = exp_rand_tree_size self.rf = RuleFit(rfmode=mode, tree_size=max_leafs, max_rules=max_rules, tree_generator=tree_generator, exp_rand_tree_size=True, fit_lasso=False, Cs=Cs, cv=cv)
import numpy as np import pandas as pd from sklearn.ensemble import GradientBoostingRegressor from rulefit import RuleFit boston_data = pd.read_csv("boston.csv", index_col=0) y = boston_data.medv.values X = boston_data.drop("medv", axis=1) features = X.columns X = X.as_matrix() gb = GradientBoostingRegressor(n_estimators=100, max_depth=3, learning_rate=0.01) rf = RuleFit(gb) rf.fit(X, y, feature_names=features) rules = rf.get_rules() rules = rules[rules.coef != 0].sort("support")
# 交叉验证 rs = KFold(n_splits=args.kfolds, shuffle=True, random_state=args.randomseed) # 生成 k-fold 训练集、测试集索引 cv_index_set = rs.split(y) k_fold_step = 1 # 初始化折数 # 暂存每次选中的测试集和对应预测结果 test_cache = pred_cache = np.array([], dtype=np.int) # 迭代训练 k-fold 交叉验证 for train_index, test_index in cv_index_set: print("\nFold:", k_fold_step) # 初始化 estimator 训练集进入模型 rf = RuleFit(tree_size=args.treesize, rfmode=args.rfmode, max_rules=args.maxrules, random_state=args.randomseed) rf.fit(X[train_index], y[train_index], features) # 测试集验证 y_pred = rf.predict(X[test_index]) # 计算测试集 ACC accTest = accuracy_score(y[test_index], y_pred) print("\nFold:", k_fold_step, "Test Accuracy:", "{:.6f}".format(accTest), "Test Size:", test_index.size) # 暂存每次选中的测试集和预测结果 test_cache = np.concatenate((test_cache, y[test_index])) pred_cache = np.concatenate((pred_cache, y_pred)) print( "\n=========================================================================" ) # 每个fold训练结束后次数 +1
rgb = RuleFit(Cs=None, cv=3, exp_rand_tree_size=True, lin_standardise=True, lin_trim_quantile=0.025, max_rules=2000, memory_par=0.01, model_type='rl', random_state=None, rfmode='regress', sample_fract='default', tree_generator=GradientBoostingRegressor( alpha=0.9, criterion='friedman_mse', init=None, learning_rate=0.02, loss='ls', max_depth=100, max_features=None, max_leaf_nodes=15, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=500, n_iter_no_change=None, presort='auto', random_state=572, subsample=0.46436099318265595, tol=0.0001, validation_fraction=0.1, verbose=0, warm_start=False), tree_size=3)
from sklearn.ensemble import GradientBoostingRegressor,GradientBoostingClassifier, RandomForestClassifier, RandomForestRegressor from rulefit import RuleFit boston_data = pd.read_csv("boston.csv", index_col=0) y = boston_data.medv.values X = boston_data.drop("medv", axis=1) features = X.columns X = X.values typ = 'regressor' #regressor or classifier if typ == 'regressor': rf = RuleFit( rfmode='regress', tree_generator=RandomForestRegressor() ) rf.fit(X, y, feature_names=features) y_pred = rf.predict(X) insample_rmse = np.sqrt(np.sum((y_pred - y)**2)/len(y)) elif typ == 'classifier': y_class = y.copy() y_class[y_class < 21] = -1 y_class[y_class >= 21] = +1 N = X.shape[0] rf = RuleFit( rfmode='classify', tree_generator=RandomForestClassifier() ) rf.fit(X, y_class, feature_names=features) y_pred = rf.predict(X) y_proba = rf.predict_proba(X)
# -*- coding: utf-8 -*- """ Created on Sun Nov 25 23:54:35 2018 @author: Melanie """ import numpy as np import pandas as pd from rulefit import RuleFit boston_data = pd.read_csv("prism_numeric.csv", index_col=0) y = boston_data.medv.values X = boston_data.drop("medv", axis=1) features = X.columns X = X.as_matrix() rf = RuleFit() rf.fit(X, y, feature_names=features) rf.predict(X) rules = rf.get_rules() rules = rules[rules.coef != 0].sort_values("support", ascending=False) print(rules)
boston_data = pd.read_csv("boston.csv", index_col=0) y = boston_data.medv.values X = boston_data.drop("medv", axis=1) features = X.columns X = X.as_matrix() typ = 'classifier' #regressor or classifier if typ == 'regressor': rf = RuleFit(tree_size=4, sample_fract='default', max_rules=2000, memory_par=0.01, tree_generator=None, rfmode='regress', lin_trim_quantile=0.025, lin_standardise=True, exp_rand_tree_size=True, random_state=1) rf.fit(X, y, feature_names=features) y_pred = rf.predict(X) insample_rmse = np.sqrt(np.sum((y_pred - y)**2) / len(y)) elif typ == 'classifier': y_class = y.copy() y_class[y_class < 21] = -1 y_class[y_class >= 21] = +1 N = X.shape[0] rf = RuleFit(tree_size=4, sample_fract='default', max_rules=2000,
data = np.load('data.npy') target = np.load('target.npy') feature_name = ['QNH', 'TEMP', 'RH', 'absolute_temp', 'WS2A', 'CW2A'] train = data[200:, :] test = data[:200, :] train_target = target[200:] test_target = target[:200] from sklearn.ensemble import GradientBoostingRegressor gb = GradientBoostingRegressor(n_estimators=500, max_depth=10, learning_rate=0.01) relu_fit = RuleFit() relu_fit.max_iter = 4000 relu_fit.tree_generator = gb relu_fit.fit(train, train_target, feature_names=feature_name) f = relu_fit.predict(test) ff = relu_fit.predict(train) rule = relu_fit.get_rules() truth = 0 for i in range(test_target.shape[0]): if abs(test_target[i] - f[i]) / test_target[i] < 0.1: truth += 1 print("truth: ", truth / test_target.shape[0]) #print(rule) ruleset = pd.DataFrame(data=rule) writer = pd.ExcelWriter('./rules.xlsx')
def fitrf(train, labels): r = RuleFit() r.fit(train, labels) return r