ここで条件付きスペースを間違って設定している可能性があることは十分に認めますが、何らかの理由で、これをまったく機能させることができません。hyperopt を使用してロジスティック回帰モデルを調整しようとしていますが、ソルバーによっては、調査する必要がある他のパラメーターがいくつかあります。liblinear ソルバーを選択するとペナルティを選択でき、ペナルティによってはデュアルも選択できます。ただし、この検索スペースでハイパーオプトを実行しようとすると、以下に示すように辞書全体を渡すため、エラーが発生し続けます。何か案は？
私が得ているエラーは
ValueError: Logistic Regression supports only liblinear, newton-cg, lbfgs and sag solvers, got {'solver': 'sag'}'
この形式は、ランダム フォレスト検索スペースを設定するときに機能したので、途方に暮れています。
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.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
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))*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
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