我完全承认我可能在这里设置错误的条件空间但由于某种原因，我根本无法让它运行起来。我试图使用hyperopt来调整逻辑回归模型，并且根据求解器还有一些其他需要探索的参数。如果你选择了liblinear解算器，你可以选择惩罚，根据惩罚，你也可以选择双重。当我尝试在这个搜索空间上运行hyperopt时，它不断给我一个错误，因为它传递整个字典，如下所示。有任何想法吗？我得到的错误是'ValueError：Logistic回归仅支持liblinear，newton-cg，lbfgs和sag求解器，得到{'solver'：'sag'}'这种格式在设置随机林搜索空间时起作用，所以我'我不知所措。
import numpy as np
import scipy as sp
import pandas as pd
pd.options.display.max_columns = None
pd.options.display.max_rows = None
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set(style="white")
import pyodbc
import statsmodels as sm
from pandasql import sqldf
import math
from tqdm import tqdm
import pickle
from sklearn.preprocessing import RobustScaler, OneHotEncoder, MinMaxScaler
from sklearn.utils import shuffle
from sklearn.cross_validation import KFold, StratifiedKFold, cross_val_score, cross_val_predict, train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import StratifiedKFold as StratifiedKFoldIt
#from sklearn.grid_search import GridSearchCV, RandomizedSearchCV
from sklearn.feature_selection import RFECV, VarianceThreshold, SelectFromModel, SelectKBest
from sklearn.decomposition import PCA, IncrementalPCA, FactorAnalysis
from sklearn.calibration import CalibratedClassifierCV
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, GradientBoostingClassifier, AdaBoostClassifier, BaggingClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV, SGDClassifier
from sklearn.metrics import precision_recall_curve, precision_score, recall_score, accuracy_score, classification_report, confusion_matrix, f1_score, log_loss
from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN
from imblearn.under_sampling import RandomUnderSampler, ClusterCentroids, NearMiss, NeighbourhoodCleaningRule, OneSidedSelection
#import lightgbm as lgbm
from xgboost.sklearn import XGBClassifier
from hyperopt import fmin, tpe, hp, Trials, STATUS_OK
space4lr = {
'C': hp.uniform('C', .0001, 100.0),
'solver' : hp.choice('solver', [
{'solver' : 'newton-cg',},
{'solver' : 'lbfgs',},
{'solver' : 'sag'},
{'solver' : 'liblinear', 'penalty' : hp.choice('penalty', [
{'penalty' : 'l1'},
{'penalty' : 'l2', 'dual' : hp.choice('dual', [True, False])}]
)},
]),
'fit_intercept': hp.choice('fit_intercept', ['True', 'False']),
'class_weight': hp.choice('class_weight', ['balanced', None]),
'max_iter': 50000,
'random_state': 84,
'n_jobs': 8
}
lab = 0
results = pd.DataFrame()
for i in feature_elims:
target = 'Binary_over_3'
alt_targets = ['year2_PER', 'year2_GP' ,'year2_Min', 'year2_EFF' ,'year2_WS/40' ,'year2_Pts/Poss' ,'Round' ,'GRZ_Pick'
,'GRZ_Player_Rating' ,'Binary_over_2', 'Binary_over_3' ,'Binary_over_4' ,'Binary_5' ,'Draft_Strength']
#alt_targets.remove(target)
nondata_columns = ['display_name' ,'player_global_id', 'season' ,'season_' ,'team_global_id', 'birth_date', 'Draft_Day']
nondata_columns.extend(alt_targets)
AGG_SET_CART_PERC = sqldf("""SELECT * FROM AGG_SET_PLAYED_ADJ_SOS_Jan1 t1
LEFT JOIN RANKINGS t2 ON t1.[player_global_id] = t2.[player_global_id]
LEFT JOIN Phys_Training t3 ON t1.[player_global_id] = t3.[player_global_id]""")
AGG_SET_CART_PERC['HS_RSCI'] = AGG_SET_CART_PERC['HS_RSCI'].fillna(110)
AGG_SET_CART_PERC['HS_Avg_Rank'] = AGG_SET_CART_PERC['HS_Avg_Rank'].fillna(1)
AGG_SET_CART_PERC['HS_years_ranked'] = AGG_SET_CART_PERC['HS_years_ranked'].fillna(0)
AGG_SET_CART_PERC = shuffle(AGG_SET_CART_PERC, random_state=8675309)
rus = RandomUnderSampler(random_state=8675309)
ros = RandomOverSampler(random_state=8675309)
rs = RobustScaler()
X = AGG_SET_CART_PERC
y = X[target]
X = pd.DataFrame(X.drop(nondata_columns, axis=1))
position = pd.get_dummies(X['position'])
for idx, row in position.iterrows():
if row['F/C'] == 1:
row['F'] = 1
row['C'] = 1
if row['G/F'] == 1:
row['G'] = 1
row['F'] = 1
position = position.drop(['F/C', 'G/F'], axis=1)
X = pd.concat([X, position], axis=1).drop(['position'], axis=1)
X = rs.fit_transform(X, y=None)
X = i.transform(X)
def hyperopt_train_test(params):
clf = LogisticRegression(**params)
#cvs = cross_val_score(xgbc, X, y, scoring='recall', cv=skf).mean()
skf = StratifiedKFold(y, n_folds=6, shuffle=False, random_state=1)
metrics = []
tuning_met = []
accuracy = []
precision = []
recall = []
f1 = []
log = []
for i, (train, test) in enumerate(skf):
X_train = X[train]
y_train = y[train]
X_test = X[test]
y_test = y[test]
X_train, y_train = ros.fit_sample(X_train, y_train)
X_train, y_train = rus.fit_sample(X_train, y_train)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
#tuning_met.append(precision_score(y_test, y_pred))
tuning_met.append((((precision_score(y_test, y_pred))*4) + recall_score(y_test, y_pred))/5)
accuracy.append(accuracy_score(y_test, y_pred))
precision.append(precision_score(y_test, y_pred))
recall.append(recall_score(y_test, y_pred))
f1.append(f1_score(y_test, y_pred))
log.append(log_loss(y_test, y_pred))
metrics.append(sum(tuning_met) / len(tuning_met))
metrics.append(sum(accuracy) / len(accuracy))
metrics.append(sum(precision) / len(precision))
metrics.append(sum(recall) / len(recall))
metrics.append(sum(f1) / len(f1))
metrics.append(sum(log) / len(log))
return(metrics)
best = 0
count = 0
def f(params):
global best, count, results, lab, met
met = hyperopt_train_test(params.copy())
met.append(params)
met.append(featureset_labels[lab])
acc = met[0]
results = results.append([met])
if acc > best:
print(featureset_labels[lab],'new best:', acc, 'Accuracy:', met[1], 'Precision:', met[2], 'Recall:', met[3], 'using', params, """
""")
best = acc
#if results.empty is False & results.count() >= lab:
# results.drop(results.index[lab])
#results = results.append([met])
else:
print(acc, featureset_labels[lab], count)
count = count + 1
return {'loss': -acc, 'status': STATUS_OK}
trials = Trials()
best = fmin(f, space4lr, algo=tpe.suggest, max_evals=1000, trials=trials)
print(featureset_labels[lab], ' best:')
print(best, """
""")
lab = lab + 1
虽然答复太晚了，但昨天我遇到了这个问题。下面提到的代码片段，我认为会帮助你。
space = hp.choice('classifier',[
{
'model': LogisticRegression,
'param':
{
'hyper_param_groups' :hp.choice('hyper_param_groups',
[
{
'penalty':hp.choice('penalty_block1', ['l2']),
'solver':hp.choice('solver_block1', ['newton-cg', 'sag', 'saga', 'lbfgs']),
'multi_class':hp.choice('multi_class', ['ovr', 'multinomial']),
},
{
'penalty':hp.choice('penalty_block2', ['l2']),
'solver':hp.choice('solver_block2', ['liblinear']),
'multi_class':hp.choice('multi_class_block2', ['ovr']),
},
{
'penalty':hp.choice('penalty_block3', ['l1']),
'solver':hp.choice('solver_block3', ['saga']),
'multi_class':hp.choice('multi_class_block3', ['ovr', 'multinomial']),
},
]),
'dual':hp.choice('dual', [False]),
'class_weight':hp.choice('class_weight', ['balanced', None]),
'random_state':hp.choice('random_state', [10,267]),
'max_iter':hp.choice('max_iter', [100,500]),
'verbose':hp.choice('verbose', [0])
}
}])
以及如何在可调用方法中使用它：
penalty = args['param']['hyper_param_groups']['penalty']
solver = args['param']['hyper_param_groups']['solver']
multi_class = args['param']['hyper_param_groups']['multi_class']
dual = args['param']['dual']
class_weight = args['param']['class_weight']
random_state = args['param']['random_state']
max_iter = args['param']['max_iter']
verbose = args['param']['verbose']