大家好，又见面了，我是你们的朋友全栈君。
{
“cells”: [
{
“cell_type”: “code”,
“execution_count”: 23,
“metadata”: {
“scrolled”: true
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“outputs”: [
{
“name”: “stdout”,
“output_type”: “stream”,
“text”: [
“\n”,
“RangeIndex: 768 entries, 0 to 767\n”,
“Data columns (total 9 columns):\n”,
“Pregnancies 768 non-null int64\n”,
“Glucose 768 non-null int64\n”,
“BloodPressure 768 non-null int64\n”,
“SkinThickness 768 non-null int64\n”,
“Insulin 768 non-null int64\n”,
“BMI 768 non-null float64\n”,
“DiabetesPedigreeFunction 768 non-null float64\n”,
“Age 768 non-null int64\n”,
“Outcome 768 non-null int64\n”,
“dtypes: float64(2), int64(7)\n”,
“memory usage: 54.1 KB\n”
]
},
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” \n”,
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\n”,
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\n”,
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Pregnancies\n”,
”
Glucose\n”,
”
BloodPressure\n”,
”
SkinThickness\n”,
”
Insulin\n”,
”
BMI\n”,
”
DiabetesPedigreeFunction\n”,
”
Age\n”,
”
Outcome\n”,
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\n”,
”
\n”,
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\n”,
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\n”,
”
0\n”,
”
6\n”,
”
148\n”,
”
72\n”,
”
35\n”,
”
0\n”,
”
33.6\n”,
”
0.627\n”,
”
50\n”,
”
1\n”,
”
\n”,
”
\n”,
”
1\n”,
”
1\n”,
”
85\n”,
”
66\n”,
”
29\n”,
”
0\n”,
”
26.6\n”,
”
0.351\n”,
”
31\n”,
”
0\n”,
”
\n”,
”
\n”,
”
2\n”,
”
8\n”,
”
183\n”,
”
64\n”,
”
0\n”,
”
0\n”,
”
23.3\n”,
”
0.672\n”,
”
32\n”,
”
1\n”,
”
\n”,
”
\n”,
”
3\n”,
”
1\n”,
”
89\n”,
”
66\n”,
”
23\n”,
”
94\n”,
”
28.1\n”,
”
0.167\n”,
”
21\n”,
”
0\n”,
”
\n”,
”
\n”,
”
4\n”,
”
0\n”,
”
137\n”,
”
40\n”,
”
35\n”,
”
168\n”,
”
43.1\n”,
”
2.288\n”,
”
33\n”,
”
1\n”,
”
\n”,
”
\n”,
“
\n”,
“
”
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“text/plain”: [
” Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n”,
“0 6 148 72 35 0 33.6 \n”,
“1 1 85 66 29 0 26.6 \n”,
“2 8 183 64 0 0 23.3 \n”,
“3 1 89 66 23 94 28.1 \n”,
“4 0 137 40 35 168 43.1 \n”,
“\n”,
” DiabetesPedigreeFunction Age Outcome \n”,
“0 0.627 50 1 \n”,
“1 0.351 31 0 \n”,
“2 0.672 32 1 \n”,
“3 0.167 21 0 \n”,
“4 2.288 33 1 “
]
},
“execution_count”: 23,
“metadata”: {},
“output_type”: “execute_result”
}
],
“source”: [
“# 导入必要模块\n”,
“import numpy as numpy\n”,
“import pandas as pandas\n”,
“import matplotlib.pyplot as matplot\n”,
“import seaborn as seaborn\n”,
“from sklearn.linear_model import LogisticRegression\n”,
“from sklearn.model_selection import GridSearchCV\n”,
“\n”,
“#竞赛的评价指标为logloss\n”,
“from sklearn.metrics import log_loss \n”,
“%matplotlib inline\n”,
“data=pandas.read_csv(‘diabetes.csv’);\n”,
“#数据信息\n”,
“data.info()\n”,
“#前五行\n”,
“data.head()”
]
},
{
“cell_type”: “code”,
“execution_count”: 24,
“metadata”: {
“scrolled”: true
},
“outputs”: [
{
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“\n”,
” .dataframe thead th {\n”,
” text-align: right;\n”,
” }\n”,
“\n”,
“
” \n”,
”
\n”,
”
\n”,
”
Pregnancies\n”,
”
Glucose\n”,
”
BloodPressure\n”,
”
SkinThickness\n”,
”
Insulin\n”,
”
BMI\n”,
”
DiabetesPedigreeFunction\n”,
”
Age\n”,
”
Outcome\n”,
”
\n”,
”
\n”,
”
\n”,
”
\n”,
”
count\n”,
”
768.000000\n”,
”
768.000000\n”,
”
768.000000\n”,
”
768.000000\n”,
”
768.000000\n”,
”
768.000000\n”,
”
768.000000\n”,
”
768.000000\n”,
”
768.000000\n”,
”
\n”,
”
\n”,
”
mean\n”,
”
3.845052\n”,
”
120.894531\n”,
”
69.105469\n”,
”
20.536458\n”,
”
79.799479\n”,
”
31.992578\n”,
”
0.471876\n”,
”
33.240885\n”,
”
0.348958\n”,
”
\n”,
”
\n”,
”
std\n”,
”
3.369578\n”,
”
31.972618\n”,
”
19.355807\n”,
”
15.952218\n”,
”
115.244002\n”,
”
7.884160\n”,
”
0.331329\n”,
”
11.760232\n”,
”
0.476951\n”,
”
\n”,
”
\n”,
”
min\n”,
”
0.000000\n”,
”
0.000000\n”,
”
0.000000\n”,
”
0.000000\n”,
”
0.000000\n”,
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0.000000\n”,
”
0.078000\n”,
”
21.000000\n”,
”
0.000000\n”,
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\n”,
”
\n”,
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25%\n”,
”
1.000000\n”,
”
99.000000\n”,
”
62.000000\n”,
”
0.000000\n”,
”
0.000000\n”,
”
27.300000\n”,
”
0.243750\n”,
”
24.000000\n”,
”
0.000000\n”,
”
\n”,
”
\n”,
”
50%\n”,
”
3.000000\n”,
”
117.000000\n”,
”
72.000000\n”,
”
23.000000\n”,
”
30.500000\n”,
”
32.000000\n”,
”
0.372500\n”,
”
29.000000\n”,
”
0.000000\n”,
”
\n”,
”
\n”,
”
75%\n”,
”
6.000000\n”,
”
140.250000\n”,
”
80.000000\n”,
”
32.000000\n”,
”
127.250000\n”,
”
36.600000\n”,
”
0.626250\n”,
”
41.000000\n”,
”
1.000000\n”,
”
\n”,
”
\n”,
”
max\n”,
”
17.000000\n”,
”
199.000000\n”,
”
122.000000\n”,
”
99.000000\n”,
”
846.000000\n”,
”
67.100000\n”,
”
2.420000\n”,
”
81.000000\n”,
”
1.000000\n”,
”
\n”,
”
\n”,
“
\n”,
“
”
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“text/plain”: [
” Pregnancies Glucose BloodPressure SkinThickness Insulin \\\n”,
“count 768.000000 768.000000 768.000000 768.000000 768.000000 \n”,
“mean 3.845052 120.894531 69.105469 20.536458 79.799479 \n”,
“std 3.369578 31.972618 19.355807 15.952218 115.244002 \n”,
“min 0.000000 0.000000 0.000000 0.000000 0.000000 \n”,
“25% 1.000000 99.000000 62.000000 0.000000 0.000000 \n”,
“50% 3.000000 117.000000 72.000000 23.000000 30.500000 \n”,
“75% 6.000000 140.250000 80.000000 32.000000 127.250000 \n”,
“max 17.000000 199.000000 122.000000 99.000000 846.000000 \n”,
“\n”,
” BMI DiabetesPedigreeFunction Age Outcome \n”,
“count 768.000000 768.000000 768.000000 768.000000 \n”,
“mean 31.992578 0.471876 33.240885 0.348958 \n”,
“std 7.884160 0.331329 11.760232 0.476951 \n”,
“min 0.000000 0.078000 21.000000 0.000000 \n”,
“25% 27.300000 0.243750 24.000000 0.000000 \n”,
“50% 32.000000 0.372500 29.000000 0.000000 \n”,
“75% 36.600000 0.626250 41.000000 1.000000 \n”,
“max 67.100000 2.420000 81.000000 1.000000 “
]
},
“execution_count”: 24,
“metadata”: {},
“output_type”: “execute_result”
}
],
“source”: [
“#数据描述\n”,
“data.describe()”
]
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“### 糖尿病人数直方图”
]
},
{
“cell_type”: “code”,
“execution_count”: 25,
“metadata”: {
“scrolled”: true
},
“outputs”: [
{
“data”: {
“image/png”: 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\n”,
“text/plain”: [
“
]
},
“metadata”: {
“needs_background”: “light”
},
“output_type”: “display_data”
}
],
“source”: [
“seaborn.countplot(data.Outcome);”
]
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“### 选择合适得特征值，分割数据”
]
},
{
“cell_type”: “code”,
“execution_count”: 26,
“metadata”: {},
“outputs”: [
{
“data”: {
“text/plain”: [
“(614, 8)”
]
},
“execution_count”: 26,
“metadata”: {},
“output_type”: “execute_result”
}
],
“source”: [
“Y_Feature=data[‘Outcome’]\n”,
“X_Feature =data.drop(‘Outcome’, axis = 1)\n”,
“\n”,
“from sklearn.model_selection import train_test_split\n”,
“\n”,
“#random select 20% as test data\n”,
“x_train, x_test, y_train, y_test = train_test_split(X_Feature, Y_Feature, random_state=33, test_size=0.2)\n”,
“x_train.shape”
]
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“### 标准化处理”
]
},
{
“cell_type”: “code”,
“execution_count”: 27,
“metadata”: {},
“outputs”: [],
“source”: [
“from sklearn.preprocessing import StandardScaler\n”,
“ss_X = StandardScaler()\n”,
“x_train=ss_X.fit_transform(x_train)\n”,
“x_test = ss_X.fit_transform(x_test)”
]
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“### 缺省的Logistic正则”
]
},
{
“cell_type”: “code”,
“execution_count”: 28,
“metadata”: {},
“outputs”: [
{
“name”: “stdout”,
“output_type”: “stream”,
“text”: [
“logloss of each fold is: [0.46129644 0.46510588 0.56426612 0.44377717 0.46617809]\n”,
“cv logloss is: 0.48012474004701106\n”
]
},
{
“data”: {
“text/plain”: [
“0.7402597402597403”
]
},
“execution_count”: 28,
“metadata”: {},
“output_type”: “execute_result”
}
],
“source”: [
“from sklearn.linear_model import LogisticRegression\n”,
“lr= LogisticRegression()\n”,
“from sklearn.cross_validation import cross_val_score\n”,
“loss = cross_val_score(lr, x_train, y_train, cv=5, scoring=’neg_log_loss’)\n”,
“print ‘logloss of each fold is: ‘,-loss\n”,
“print’cv logloss is:’, -loss.mean()\n”,
“\n”,
“lr.fit(x_train,y_train)\n”,
“lr.score(x_test,y_test)”
]
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“采用5这交叉验证，发现负损失稳定在0.4左右,准确度为0.73,下面用GridSearchCV进行参数调优”
]
},
{
“cell_type”: “code”,
“execution_count”: 29,
“metadata”: {
“scrolled”: true
},
“outputs”: [
{
“data”: {
“text/plain”: [
“GridSearchCV(cv=5, error_score=’raise’,\n”,
” estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n”,
” intercept_scaling=1, max_iter=100, multi_class=’ovr’, n_jobs=1,\n”,
” penalty=’l2′, random_state=None, solver=’liblinear’, tol=0.0001,\n”,
” verbose=0, warm_start=False),\n”,
” fit_params=None, iid=True, n_jobs=1,\n”,
” param_grid={‘penalty’: [‘l1’, ‘l2’], ‘C’: [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n”,
” pre_dispatch=’2*n_jobs’, refit=True, return_train_score=’warn’,\n”,
” scoring=’neg_log_loss’, verbose=0)”
]
},
“execution_count”: 29,
“metadata”: {},
“output_type”: “execute_result”
}
],
“source”: [
“from sklearn.model_selection import GridSearchCV\n”,
“from sklearn.linear_model import LogisticRegression\n”,
“\n”,
“penaltys = [‘l1′,’l2’]\n”,
“Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n”,
“tuned_parameters = dict(penalty = penaltys, C = Cs)\n”,
“\n”,
“lr_penalty= LogisticRegression()\n”,
“grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring=’neg_log_loss’)\n”,
“grid.fit(x_train,y_train)”
]
},
{
“cell_type”: “code”,
“execution_count”: 30,
“metadata”: {},
“outputs”: [
{
“data”: {
“text/plain”: [
“{‘mean_fit_time’: array([0.00282516, 0.00275798, 0.00244303, 0.00339441, 0.00315719,\n”,
” 0.003087 , 0.00379777, 0.00329165, 0.00360203, 0.00362625,\n”,
” 0.00277553, 0.0023922 , 0.00354481, 0.00334883]),\n”,
” ‘mean_score_time’: array([0.00222125, 0.00185637, 0.00199313, 0.00197988, 0.00186162,\n”,
” 0.00181241, 0.00183372, 0.00223641, 0.00215921, 0.00247059,\n”,
” 0.0017406 , 0.001439 , 0.00229459, 0.00270238]),\n”,
” ‘mean_test_score’: array([-0.69314718, -0.64214833, -0.6721329 , -0.52844007, -0.48658742,\n”,
” -0.47999943, -0.48043706, -0.48017599, -0.48089965, -0.48086932,\n”,
” -0.48095236, -0.48095123, -0.48095803, -0.48095956]),\n”,
” ‘mean_train_score’: array([-0.69314718, -0.6412946 , -0.67079105, -0.52380684, -0.47502596,\n”,
” -0.46674403, -0.46228807, -0.46214818, -0.46206767, -0.46206619,\n”,
” -0.46206531, -0.4620653 , -0.46206529, -0.46206529]),\n”,
” ‘param_C’: masked_array(data=[0.001, 0.001, 0.01, 0.01, 0.1, 0.1, 1, 1, 10, 10, 100,\n”,
” 100, 1000, 1000],\n”,
” mask=[False, False, False, False, False, False, False, False,\n”,
” False, False, False, False, False, False],\n”,
” fill_value=’?’,\n”,
” dtype=object),\n”,
” ‘param_penalty’: masked_array(data=[‘l1’, ‘l2’, ‘l1’, ‘l2’, ‘l1’, ‘l2’, ‘l1’, ‘l2’, ‘l1’,\n”,
” ‘l2’, ‘l1’, ‘l2’, ‘l1’, ‘l2’],\n”,
” mask=[False, False, False, False, False, False, False, False,\n”,
” False, False, False, False, False, False],\n”,
” fill_value=’?’,\n”,
” dtype=object),\n”,
” ‘params’: [{‘C’: 0.001, ‘penalty’: ‘l1’},\n”,
” {‘C’: 0.001, ‘penalty’: ‘l2’},\n”,
” {‘C’: 0.01, ‘penalty’: ‘l1’},\n”,
” {‘C’: 0.01, ‘penalty’: ‘l2’},\n”,
” {‘C’: 0.1, ‘penalty’: ‘l1’},\n”,
” {‘C’: 0.1, ‘penalty’: ‘l2’},\n”,
” {‘C’: 1, ‘penalty’: ‘l1’},\n”,
” {‘C’: 1, ‘penalty’: ‘l2’},\n”,
” {‘C’: 10, ‘penalty’: ‘l1’},\n”,
” {‘C’: 10, ‘penalty’: ‘l2’},\n”,
” {‘C’: 100, ‘penalty’: ‘l1’},\n”,
” {‘C’: 100, ‘penalty’: ‘l2’},\n”,
” {‘C’: 1000, ‘penalty’: ‘l1’},\n”,
” {‘C’: 1000, ‘penalty’: ‘l2’}],\n”,
” ‘rank_test_score’: array([14, 12, 13, 11, 10, 1, 3, 2, 5, 4, 7, 6, 8, 9],\n”,
” dtype=int32),\n”,
” ‘split0_test_score’: array([-0.69314718, -0.64371675, -0.66946739, -0.52816851, -0.48618155,\n”,
” -0.4678555 , -0.46345904, -0.46129644, -0.46108568, -0.46088502,\n”,
” -0.46086642, -0.4608487 , -0.46084313, -0.46084513]),\n”,
” ‘split0_train_score’: array([-0.69314718, -0.64085132, -0.66189542, -0.52529099, -0.47823867,\n”,
” -0.47085398, -0.46684285, -0.46669378, -0.46662379, -0.46662226,\n”,
” -0.46662151, -0.46662149, -0.46662149, -0.46662148]),\n”,
” ‘split1_test_score’: array([-0.69314718, -0.64113725, -0.67517494, -0.52518603, -0.47166225,\n”,
” -0.47077815, -0.46426366, -0.46510588, -0.4646352 , -0.46473286,\n”,
” -0.46468867, -0.46469927, -0.46469489, -0.46469595]),\n”,
” ‘split1_train_score’: array([-0.69314718, -0.64188719, -0.67797602, -0.52532854, -0.47970513,\n”,
” -0.47076994, -0.4669271 , -0.46678157, -0.46671611, -0.4667146 ,\n”,
” -0.4667139 , -0.46671388, -0.46671388, -0.46671388]),\n”,
” ‘split2_test_score’: array([-0.69314718, -0.64575466, -0.6680453 , -0.5512625 , -0.54752984,\n”,
” -0.54301469, -0.56469495, -0.56426612, -0.56847801, -0.56834734,\n”,
” -0.56879909, -0.56879096, -0.56883272, -0.5688357 ]),\n”,
” ‘split2_train_score’: array([-0.69314718, -0.63908575, -0.66162164, -0.5135644 , -0.45564429,\n”,
” -0.44783203, -0.44194643, -0.44184498, -0.44173178, -0.44173047,\n”,
” -0.44172921, -0.4417292 , -0.44172919, -0.44172919]),\n”,
” ‘split3_test_score’: array([-0.69314718, -0.63856226, -0.67581323, -0.50975335, -0.45503223,\n”,
” -0.44706595, -0.44365688, -0.44377717, -0.44397598, -0.44400066,\n”,
” -0.44403119, -0.4440332 , -0.44403654, -0.44403656]),\n”,
” ‘split3_train_score’: array([-0.69314718, -0.64333601, -0.67874616, -0.5309552 , -0.48382671,\n”,
” -0.47509784, -0.47066678, -0.47052445, -0.4704446 , -0.47044314,\n”,
” -0.47044228, -0.47044226, -0.47044225, -0.47044225]),\n”,
” ‘split4_test_score’: array([-0.69314718, -0.64152376, -0.67221592, -0.52767402, -0.47216067,\n”,
” -0.47104103, -0.46583102, -0.46617809, -0.46606368, -0.46612355,\n”,
” -0.46611895, -0.4661268 , -0.46612564, -0.46612722]),\n”,
” ‘split4_train_score’: array([-0.69314718, -0.64131272, -0.67371601, -0.52389506, -0.47771499,\n”,
” -0.46916638, -0.46505718, -0.46489609, -0.46482209, -0.46482048,\n”,
” -0.46481966, -0.46481964, -0.46481963, -0.46481963]),\n”,
” ‘std_fit_time’: array([0.00086323, 0.00059983, 0.00060702, 0.00071247, 0.00065826,\n”,
” 0.00033766, 0.00033068, 0.00036014, 0.00111406, 0.00057291,\n”,
” 0.00033485, 0.00048554, 0.0004031 , 0.00028537]),\n”,
” ‘std_score_time’: array([9.43189981e-04, 4.28180928e-04, 3.54302375e-04, 4.90339327e-05,\n”,
” 1.43874296e-04, 7.93332081e-05, 1.74334456e-04, 5.03879477e-04,\n”,
” 4.15763520e-04, 8.80480163e-04, 1.15419279e-04, 4.23557745e-04,\n”,
” 2.53882737e-04, 3.60673921e-04]),\n”,
” ‘std_test_score’: array([0. , 0.00243715, 0.00305426, 0.0132657 , 0.03206037,\n”,
” 0.03276814, 0.04294169, 0.04285084, 0.04453539, 0.04448689,\n”,
” 0.04466504, 0.04466183, 0.04467864, 0.04467945]),\n”,
” ‘std_train_score’: array([0. , 0.00138524, 0.00757197, 0.00566626, 0.00992521,\n”,
” 0.00965834, 0.01033363, 0.01031565, 0.01033126, 0.01033118,\n”,
” 0.01033136, 0.01033136, 0.01033136, 0.01033136])}”
]
},
“execution_count”: 30,
“metadata”: {},
“output_type”: “execute_result”
}
],
“source”: [
“grid.cv_results_”
]
},
{
“cell_type”: “code”,
“execution_count”: 31,
“metadata”: {},
“outputs”: [
{
“data”: {
“image/png”: 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r/+RiYG5Vjlfel/3FF+y78SZskZHEzfmIRr17n7T/WMkxHlv1GA+ufJAukV2Y+7u5DGs7zLOTb5kH4gfdxvDZhlRahgfRv0MzH9RCKVVdViWUlsaYNAD3d4sKygWLyDoRWS0ipUmjGZBljCkdUS8FqPBXVhGZ7D7HuoyMDG/Fr3C90X54xkwO3Hc/wQnxxM35iMC2bU8qs/3Idq754hrm75zP5PjJvD3ibWJCYzy9gLu7awBH/SJZvj2DMQmtsFVhUEillO/57B6KiCwFosvZ9UglTtPWGHNARDoAX4vIZiCnnHIVTvxgjJkJzARITEy0YIKI+smUlJA2dSrZcz8lfPRoYp55Gr/AwBP7jeGT7Z/w3NrnCA8K581L36R/TP/KXeRQMmTugPPv4MstadidhrHa3aVUreWzhGKMqXASARE5JCIxxpg0EYkB0is4xwH3924RWQ70AT4FmoiIv7uVEgsc8HoFVIUceXmk/vlu8r//nmZ33E7Un/980pNc2UXZTP1hKkv2LmFA6wE8PeBpmjWqQjdVctKJ7q4PdtKpRSg9WoV7sSZKKW+yqstrAXCDe/kG4LNTC4hIpIgEuZebAwOArcY1hd43wJVnOl75RklaGnv/cC35a9YQ8/RTtLj77pOSyYb0DUz8fCLf7PuG+/rex7+H/btqyaS0uytuECklIazZc4RxvVvpYI9K1WJWJZRpwCUisgO4xL2OiCSKyCx3mW7AOhHZiCuBTDPGbHXvexC4V0R24rqn8laNRt9AFSQns2fi1ZQcOEDbmTNocsWJSZScxsmszbO4cdGNiAjvXfYeN/a8ET+p4l+xg5vgyC7oOYEFG10NUO3uUqp2s+Q9FGNMJnDaYz7GmHXAre7l74FeFRy/G+jnyxjVyXKXLyf13vuwNYmg3VsfEtyly/F9hwsO89DKh1idtpqRcSN57ILHCAsMq94Fk5NAbHDO7/hsZjJ920XSpmnjatZCKeVL+qa8Oqsj//kPh556muBzziH2jdcJaHHiobzvU7/noe8e4ljJMR6/4HEmdJ5Q/W6p0u6uDkPYlhPA9kO5PDm2RzVroZTyNU0oqkLG6ST9+Rc48s47hA4dSusXnscvxDVwY4mzhNd+fo23t7xNpyadeOvSt+gU2ck7F07bAEf3wKD7mL8hFX8/4fL4Vt45t1LKZzShqHI5Cwo48NcHyV2yhMhJk2j50BTE5pomNzUvlb+u+CubMjZxVZereOC8B2jk38h7F09OAj9/nF1H8/lXPzO4SxRNQwLPfpxSylKaUNRp7JmZ7P/TnyjctJmWDz9E0+uvP75v8Z7FPP794xgMLwx5gRFxpw+xUi3Hu7uGsuaQ4UB2IQ9edo53r6GU8glNKOokRbt3s3/ybdgPHyb21X8SNtz1OlGhvZDn1z7PJ79+QnzzeKYPnk5sWKz3A0hdD1n7YMgUPtuQSuNAG5d0b3lasbji+71/baVUtWhCUcflr1lDyv/9GfH3p917s2kUHw/Arqxd3P/t/ezM2snNPW/mrj53EeAX4JsgkueBXwBFnUbyxWfruLR7SxoH6l9TpeoC/ZfawNy06CYA3hn5zknbsxcs4MAjjxLYti1tZrxBYGwsxhiSdibx7I/P0jigMW8Mf4MBrQf4LjhjIHk+dLyY5ftKyCm0M7aPvnuiVF2hCaWBM8Zw+PXXOfzPV2ncvz+x//wHtogI8orzeOKHJ/jfnv/RP6Y/zw58lqjGUb4NJmUd5KTAxY/y2YZUmoUEMqhTc99eUynlNZpQGphrXk12LYwEU1xM2t8fJzspiYixY4l58gkkMJDkw8nc/+39pOWncfe5d3Nzz5ur/sZ7ZSTPA1sgOXGXsnTuGn5/Xhv8bVYN5qCUqixNKA2UIyeHlD/fzbHVq2l+1100v/NPGAzvJc/mlfWvENUoindHvkvvFr3PfjJvcDpd3V2dhrNo5zGK7c4zdnd9fNsFNROXUspjmlAaIFuJkz1/+APFe/cRM+1Zmowbx5HCIzz63aOsTF3JsLbDmHrhVCKCImouqJQ1kHsAekzlszWptGvWmD5tmtTc9ZVS1aYJpYEJLHQQdbAAe0gGbWfNIqR/P9akrWHKyilkF2XzSP9HuLrr1TU/qm9yEtiCSI8Zyve71vB/QzvpyMJK1TGaUBoQ++HDtEg7htNPiPvoP9jat+O1n19j5qaZtAtvx+vDX6dr0641H1hpd1fnS1jwSy7G0KCe7vrxpk+tDkEpr9CE0oBkvPoa4oT01o2Jig7hwa9uYX36esZ1GsdD/R6icYBFo/nu+wHyDkKP8Xy2/AC9WkfQMSrUmliUUlWmCaWBKPz1V7L++19yIwLYGGf4++dXUuIo4dlBzzK6w2hrg0tOAv9gdjcdxObUdTx6eTdr41FVVp9aW/WlLjVZD00oDUT6c8/jFxbKJwOLWBxfTLeQjrww5AXahre1NjCnA7Z+Bp0vZX5yFn4CYxJ0ZGGl6iJ9yL8ByFu5kvzvviPn9yNYHO/g/F02Phj1gfXJBGDv95CfjukxnvkbDnBhx+a0CA+2OiqlVBVoQqnnjN1O+nPPEdC2LU+1/olgWzDFFyQQaKslw8EnJ0FAYzY26s++I8cY21tbJ0rVVR4lFBEZICIh7uVJIvKSiLTzbWjKG7LmfkrRjp0kTzyX3cf20SasTc289e4Jh93V3dVlBElbjhLk78fIntFWR6WUqiJPf7K8DhwTkQTgr8Be4D2fRaW8wpGXR8arrxLQJ4FnG33N4NjBNfuy4tns/Q6OHcbebRwLN6UxvFtLwoJ9NIqxUsrnPE0odmOMAcYC/zDG/AMI811YyhsyZ76JIzOThaObU+Qs5oHEB6wO6WTJSRAQwirpQ2Z+sXZ3KVXHefqUV66IPARMAgaLiA3QXyVrsZLUVI68+y5mxGDetq/ghh43EBcRZ3VYJzjssHUBdB1J0uYjhAf7M6Srj0czVkr5lKctlKuBIuAWY8xBoDXwvM+iUtWW/vIrIMJr/bKIDI7ktvjbrA7pZHtWQMERirqOYfHWQ1weH0OQv83qqJRS1eBpQsnF1dW1UkS6AL2Bj3wXlqqOgk2byFm4kCPjB7HSvpW/nPsXQgNr2ZvnyUkQGMri4niOFTsY27vhDLWiVH3laUJZAQSJSGtgGXAT8K6vglJVZ4zh0LTp+DVrxlOdttC9WXfGdhprdVgnc5TAts+h6yjmbTpMq4hg+sU1tToqpVQ1eZpQxBhzDJgAvGqMGQ/08F1Yqqpyv1pMwfr1bJnQi/2OwzzU76Ha85hwqd3fQsFRcjqOZsWOw/yudyv8/HRkYaXqOk9vyouIXABcC9zi3qYd3rWMs7iY9BdewK9Te55t/iOj2o86bYKsU+eSt0RyEgSFsyDvHBzOnYzT7i6l6gVPf3X9C/AQkGSMSRaRDsA3vgtLVcXRDz6kJCWFhaOa4+fvzz1977E6pNPZi+GXE91dXVuG0S0m3OqolFJe4FFCMcZ8a4wZA/xbREKNMbuNMX/2cWyqEuxHj3L49dex90/g3ZCfuaXnLUSH1MK3zncvh8Js0ttezvp9WYzto++eKFVfeDr0Si8R+RnYAmwVkZ9EpMr3UESkqYgsEZEd7u/ICso5RGSD+7OgzPZ3ReS3MvtqaOLz2uvwa//CeewY/xqYR+vQ1tzQ4warQypf8jwIiuC/RzsBOrKwUvWJp11eM4B7jTHtjDFtgfuAN6tx3SnAMmNMZ1xPjU2poFyBMaa3+zPmlH0PlNm3oRqx1HlFu3dzdM4cDl/Sh1WBe7kv8T6C/WvhiL32IvjlC0y3y5m3KZ1+cU2JjbRoUi+llNd5mlBCjDHH75kYY5YDIdW47lhgtnt5NjCuGudq8NKffwFpFMyzPXdzXvR5DG873OqQyrfrayjKYW/0CHZl5Gt3l1L1jKcJZbeI/E1E4tyfR4HfqnHdlsaYNAD3d4sKygWLyDoRWS0ipyadp0Vkk4i8LCJB1YilTstfvZq8b75hy6iupAbm8eB5DyJSSx/BTU6C4CZ8lNGeAJtwea8YqyNSSnmRp48N3wxMBeYBgutFx5vOdICILAXKuyv8SCXia2uMOeB+quxrEdlsjNmF64mzg0AgMBN4EHiigjgmA5MB2ratBRNKeZFxODg0/TmIbsH0uC1c1eUqujbtanVY5SsphF++xNljHPM3ZzCkSwuaNK4lc7IopbzCo4RijDkKVOqpLmNMhf0uInJIRGKMMWkiEgOkV3COA+7v3SKyHOgD7Cpt3QBFIvIOcP8Z4piJK+mQmJhoKlOH2i57/mcUbdvGFzd0JaBRCXf2vtPqkCq2cykU57ItchiHcor422jt7lKqvjljQhGRz4EKfwiXc6PcUwuAG4Bp7u/Pyrl2JHDMGFMkIs2BAcBz7n2lyUhw3X/ZUsU46iznsWNkvPIKxd3aMztmJ1N6P0RkcLkPy9UOyUnQqCnvH2pLaNBhhndraXVESikvO1sL5QUfXXca8ImI3ALsA64CEJFE4HZjzK1AN2CGiDhx3euZZozZ6j7+QxGJwtX9tgG43Udx1lqZb72NPSOD1yfY6NikExO7TrQ6pIqVFMD2/2HvcQVf/HyYET2iCQ7QgRaUqm/OmFCMMd/64qLGmExgWDnb1wG3upe/B3pVcPzFvoirrig5dIjMt97i8AVdWNV0NzP7PUOAXy2enmbHEijJZ33oReQW2RmnT3cpVS95dA9FRDZzetdXNrAOeMqdIFQNyXjlHxiHg2f7pjK0zVAuaHWB1SGdWXISNG7OOwdiiQrL5cKOza2OSCnlA54+5fU/wAH8x71+Da7upmxcw9j/zuuRqXIVJCeTPX8+ySM6czBiPzNq27S+pyrOh18XUdRjIsvWHmHS+e2w6cjCStVLniaUAcaYAWXWN4vIKmPMABGZ5IvA1OmMMaRPfw4THspz3XZxffdbaRPexuqwzmzHYig5xqqgQRQ7nDpvvFL1mKcvNoaKSP/SFRHpB5ROAWj3elSqXHnffMOxNWtYNKwJoZEt+GP8H60O6eySkyAkijf3RdO+eQjxsRFWR6SU8hFPWyi3Am+LSCiurq4c4BYRCQGe9VVw6gRTUkL6c89THBvF7M4HeLLvM4QEVGf0mxpQlAe/Lia/x9WsXpPN3cM61963+JVS1ebpi41rgV4iEoFr9sasMrs/8Ulk6iRH53xM8Z49zLo2kh4t4hndYbTVIZ3djq/AXsAyvwEYg06kpVQ95+lTXhHA34HB7vVvgSeMMdk+jE25ObKzOfzaa2T2bM3yNgf5sN+U2jetb3m2zIPQaGb81oKENgHENa/lLSqlVLV4+lPpbSAXmOj+5AC1YC7ZhuHw62/gyMnhhfMPM6bTWOKj4q0O6eyKcmHHEo62v4zkg/mM05vxStV7nt5D6WiMuaLM+lQRadBzkNSU4n37OPLhh2y7oBUHYnKYee7dVofkme2LwFHEl47zsfkJo+M1oShV33naQikQkYGlKyIyACjwTUiqrPQXXsTY/Hi570Emx0+mReOKRvqvZZKTMGGteH1XcwZ0ak5UWIOdYUCpBsPTFsodwOzSm/LAEeBGXwWlXI6tW0fu4sUsvaQZodFhXNf9OqtD8kxhDuxcwqGuk0hZX8S9I7R1olRD4OlTXhuABBEJd6/n+DQqhXE6OTT9OYqbhTE7IYvnzptKkK2O/Ja//UtwFDO/uB/BAX5c2qO8aXGUUvXN2Yavv7eC7QAYY17yQUwKyPniCwo3b+b9cSGc2/YCLm5Th8bDTE7ChLdmxq5ILunektAgTxvCSqm67Gz/0sNqJAp1EmdhIekvvczRdk1Z1i2P/9bmaX1PVZAFO5exr9MkjqY79OkupRqQsw1fP7WmAlEnHHl3Nva0NP55rT8Tz/kDnSI7WR2S57Z/Cc4S/lvYj8jGAQzuEmV1REqpGlLpt+NEZL0vAlEu9owMMmfOZEevpqR0juBPvf9kdUiVk5yEM6INs35rwuXxMQTY6sALmEopr6jKv/Y60vdSN2W8+hqOokJevTCbu3rfRURQHRpMseAo7Pqanc2HU1hidKgVpRqYqtwt/cLrUSgACrf/Stbcuaw4P4SwjrFc2eVKq0OqnG0LwWnnw7y+xEY2om+7WjzHvVLK6yrdQjHGPOqLQBSkP/ccJY0LR+UCAAAe6UlEQVQCeLffMaacNwV/vzr2dFRyEo6Idry/L5KxvVvVnQcJlFJe4VFCEZFcEck55bNfRJJEpIOvg2wI8lauJH/VKj4ZABeecyn9YvpZHVLlHDsCu5eTHDkMpxHt7lKqAfL0V+CXgAO4pgAWXFMARwPbcQ0ceZEvgmsojN3OoenTyWkRwuJznczrW+7rP7Xbts/BOHg7qw/dY8Lp3FKfOFeqofG0y2ukMWaGMSbXGJNjjJkJjDLGfAxoR3k1Zc2dS/HOXcwYWMCk+BuJDYu1OqTKS06iJKI98w821Wl+lWqgPE0oThGZKCJ+7s/EMvuMLwJrKBx5eWT881X2dAhhb++W3NrrVqtDqrz8w/DbCn4OuwgRYYwmFKUaJE8TyrXAdUA6cMi9PElEGgF3+Si2BiFzxkwcR47wxuBC7km8l8YBja0OqfK2LQDjYGZmAv3bNyUmopHVESmlLODp4JC7gd9VsPs774XTsJSkppI5ezarE4IJj+/J5e0vtzqkqklOojCiI0sPRTFtqN6MV6qh8vQpry4iskxEtrjX40VEHx+upvSXXsaBg9kDS5jSb0rdfMw2Lx32fMePjQcTaLNxWa8YqyNSSlnE0y6vN4GHgBIAY8wmXE96qSoq2LiRnC++YMF5MOjc8fRo3sPqkKpm2wIwTv6dHs/Qc6KIaBRgdURKKYt4mlAaG2PWnLLN7u1gGgpjDIemTSc/PJCvBoVwd12Z1rc8W5LID+/Ij/kt9N0TpRo4TxPKYRHpiPuJLhG5EkjzWVT1XO5XX1Hw88+8P8DOjYm307xRc6tDqprcg7B3Fd8FDiIsOICh59SR6YmVUj7h6YuNdwIzgXNEJBX4DdeTX6qSnMXFHHrhBdKiA9k9MJYXuk2yOqSq27oAMPwrvReXxUcTHGCzOiKllIU8baGkAu8ATwNzgCXADVW9qIg0FZElIrLD/V3uy5Ei0lZEFovINhHZKiJx7u3tReRH9/Efi0hgVWOpaUff/wB7SiqzLrJzf7+/EmCrw/cckpPICe/MpuIY7e5SSnmcUD7D9dhwCa4hWPKA/GpcdwqwzBjTGVjmXi/Pe8DzxphuQD9c78EATAdedh9/FLilGrHUGPvRo2S8/jqbOvkTPmAQg2MHWx1S1eUcgH0/8LXfAFqGB9G/QzOrI1JKWczTLq9YY8xIL153LCfG/5oNLAceLFtARLoD/saYJQDGmDz3dgEuBv5Q5vjHgde9GJ9PHH7tXziO5fP+0AD+ed5f6+ZjwqW2fgYYXs/oyZgLW2Hzq8N1UUp5hactlO9FpJcXr9vSGJMG4P4u725uFyBLROaJyM8i8ryI2IBmQJYxpvQpsxSgwv4WEZksIutEZF1GRoYXq1A5Rbt3c3TORyzpLQwa9Ac6NKnjgzQnJ3E0rAvbHa0Yq91dSik8b6EMBG4Ukd+AIlwjDhtjTHxFB4jIUlwjEp/qkUrENgjoA+wDPgZuBBaUU7bC8cTcA1nOBEhMTLRs3LFDzz1PYYCweFgT5vS+w6owvCM7Bfb/yKLQG+nUIpQercKtjkgpVQt4mlAuq+yJjTHDK9onIodEJMYYkyYiMZy4N1JWCvCze9gXRGQ+cD6u4fKbiIi/u5USi+u+Tq2V/8MP5C9fztyhftw06G7CA+v4D+CtnwEwI7MXV16iE2kppVw86vIyxuwt71ON6y7gxFNiN+C66X+qtUCkiES51y8GthpjDPANcOVZjq8VjMNB2rRpZDaxsfuSc5jQaYLVIVVfchIZoV3ZY2K0u0spdVylpwD2kmnAJSKyA7jEvY6IJIrILABjjAO4H1gmIptxdbO96T7+QeBeEdmJ657KWzUcv8ey58+nZPuvvDfEcN+FD2Hzq+PvamTtg5S1LCjpT992kbRpWgdHR1ZK+YQlk5YbYzKBYeVsXwfcWmZ9CXDafRp3N1itnyPXmZ/PwZdfZmdrP8IvG0lidKLVIVVf8nwA3s3pw+SLdN4TpdQJVrVQGoTMt97GHM7kw0sCuS/xfqvD8Y7kJNJCupEm0VwerwlFKXWCJhQfKTl4kIy3ZrGqmzB45B+JCa0Hw7of3QMH1vNp4XkM6tycpiF1ZoACpVQN0ITiI+mvvILDXsKSy1pyU8+brA7HO9zdXXOO9WVcH70Zr5Q6mSYUHyhITiZn/md8cR7cdMmDNPKvJ1PiJs9jf6NuHAmI5pLuLa2ORilVy2hC8TJjDAemPUNuY2H3mD6MiBthdUjekbkL0jbycUEil3ZvSeNAS57nUErVYppQvCzv668pXrueTwYK9wx5tP689LfV1d01r/A8xmp3l1KqHPprpheZ4mJSpz1DSjMh7Kor6Nasm9UheU9yEruDu1Nka8WgTnV0QjCllE9pC8WLjsyZg9l/gLmXhnBXYh2e1vdUh3fCwc18lJ/I6PgY/G3610YpdTptoXiJIyuLg6/+gy1xwoAr7qJZo3o0P0hyEgCfl5zHv7W7S9VRJSUlpKSkUFhYaHUotVZwcDCxsbEEBFRt4j9NKF5y6N//hrxjLL2pDf/u9oezH1CXJCexPbAHQSFt6NOmidXRKFUlKSkphIWFERcXV3/ubXqRMYbMzExSUlJo3759lc6hfRdeULx3L0f/8yHfxAs3jH2sbk/re6qM7ZCezEf5iYxN0JGFVd1VWFhIs2bN9O9wBUSEZs2aVasFpwnFC/ZPf5ZicbL36gsZ2Hqg1eF4V/J8DMKXjn76dJeq8yqbTK6e8QNXz/jBR9HUPtVNtppQqunYunUUf/0tCy70585hj1odjvclJ5Hs34OWrePoGBVqdTRK1WmhoSf+DY0cOZImTZowevTocsveeeed9O7dm+7du9OoUSN69+5N7969mTt3bqWuuX79ehYtWlStuD2l91CqwTid7HnqcY6EQfj1k4iLiLM6JO9K3wYZ2/i45EbGDtGBIJXypgceeIBjx44xY8aMcvf/61//AmDPnj2MHj2aDRs2VOk669evZ8uWLYwcObLKsXpKWyjVkP3558gvu1hwSRi3nvcnq8PxvuQknPjxlbMfYxI0oSjlTcOGDSMsLKxKx+7YsYMRI0bQt29fBg8ezK+//grAnDlz6NmzJwkJCQwdOpSCggKeeOIJPvzwwyq1bipLWyhV5CwoYP/zz7I3Gi644UHCAqv2F6PWMgaTnMQGvx506diJFuHBVkeklNdM/TyZrQdyzlpua5qrjCf3Ubq3Cufvv+tR7dg8MXnyZGbNmkXHjh1ZtWoVd911F4sXL2bq1KksX76cli1bkpWVRaNGjXjsscfYsmULr7zyis/j0oRSRYfenoX/4WxW3NGBaV3GWx2O96VvRQ7/yqclNzO2t7ZOlKotsrKyWL16NVdcccXxbXa7HYABAwZw/fXXc9VVVzFhQs1PN64JpQrsGRkcnjmTn7oIv//9U/hJPew5dHd3fS39mdIz2upolPIqT1sSpS2Tj2+7wJfhVIoxhubNm5d7T+XNN9/kxx9/ZOHChSQkJLBp06Yaja0e/iT0vT0vTYMSOynXDaVPiz5Wh+N9xmC2JLGWHpzbrQthwfXovRql6rjIyEhiYmJISnKNYOF0Otm4cSMAu3fv5vzzz+fJJ58kMjKS1NRUwsLCyM3NrZHYNKFUUuH27RTN/5Jl5wVw66i/WR2ObxzcjBzZyfySftrdpZSPDBo0iKuuuoply5YRGxvLV1995fGxc+bM4Y033iAhIYEePXqwcOFCAO655x569epFr169GD58OD179uTiiy9m48aN9OnTR2/K1ybGGHY8+QiFQRA2+RaiQ+ppV1ByEg78WBVwAY93jbI6GqXqjby8vOPLK1eu9OiYuLg4tmzZctK2Dh06lJuAFixYcNq2qKgo1q1bV8lIq0YTSiXkfLsc/3XJLBnVhHv732Z1OL5hDM7kJH4wPRmQ0JUgf5vVESllmdp076Qu0C4vDxm7nd1PPUZaJCTe8TeC/evpY7RpG/E7+hsL7P0Z21uHWlFKeU4TiofSPnqf4JTDrJ7QlUs6XWZ1OL6TnIQdG5tCBtIvrqnV0Sil6hDt8vKAIzeX9H/+k11thAk3T6u/o5Uag2PzPFY5ezKkT1f8/OppPZVSPqEtFA8sv3wAQbmFpN0ygnOanWN1OL5zYD22nH187jifcdrdpZSqJG2heGBXCwc7WgjXjnvM6lB8KzmJEvz5rdlFdIsJtzoapaz3zuWu75u+sDaOOkJbKB5Ycn13/ndtJyKDI60OxXeMwb55HiscvRh2bhero1GqXqrp4euTkpJ4/vnnqx23p7SF4oHQwFCgns8FkvoT/rmpfOH4HffqyMJK+Zy3hq+32+34+5f/o3z8+JodZ9CShCIiTYGPgThgDzDRGHO0nHJtgVlAG8AAo4wxe0TkXWAIkO0ueqMxpmqTBXjgnZHv+OrUtYbZ8ikl+JMZewmxkY2tDkepem/YsGEsX768SscOHDiQIUOGsHLlSiZMmED79u155plnKC4uJioqig8++IAWLVowa9as4yMNT5o0iWbNmrF27VoOHjzIiy++6PWEY1ULZQqwzBgzTUSmuNcfLKfce8DTxpglIhIKOMvse8AY49txBBoKpxP75iS+dSRwad/OVkejlO/9bwoc3Hz2cgfdgyuW3ks5k+hecNm06sVVCTk5OaxYsQKAo0ePMmbMGESEN954gxdffJHp06efdkx6ejqrVq1i8+bNTJw4sd4klLHARe7l2cByTkkoItId8DfGLAEwxuShfCNlLQH5aSwyE/hbrxiro1FKeeCaa645vrxv3z4mTpzIwYMHKSoqokuX8u+Djhs3DhEhPj6e1NRUr8dkVUJpaYxJAzDGpIlIi3LKdAGyRGQe0B5YCkwxxjjc+58WkceAZe7tRTUReH3kTJ5HCQEUdRxBk8aBVoejlO952pKoxU95hYSEHF++8847efjhhxk1ahRLly5l2rTy6xcUFHR82Rjj9Zh89pSXiCwVkS3lfMZ6eAp/YBBwP3Ae0AG40b3vIeAc9/amlN9dVhrHZBFZJyLrMjIyqlqd+svppGRTEssdCYzU7i6l6qTs7Gxat26NMYbZs2dbFofPEooxZrgxpmc5n8+AQyISA+D+Ti/nFCnAz8aY3cYYOzAfONd97jTjUgS8A/Q7QxwzjTGJxpjEqCgdOfc0+1cTVHCIpX4XMrxbS6ujUarBqM7w9ad6/PHHGT9+PEOGDKFlS+v+HVvV5bUAuAGY5v7+rJwya4FIEYkyxmQAFwPrwJWE3F1lAowDtpRzvPKAffM87CYA/26jCA7QkYWV8iVvDV//3XffnbR+xRVXnDQlcKlbb731+PIHH3xQYSzeYlVCmQZ8IiK3APuAqwBEJBG43RhzqzHGISL3A8vcieMn4E338R+KSBQgwAbg9hqvQX3gdGDfMp+vnX0Y1beT1dEoVfvUwnsntZklCcUYkwkMK2f7OuDWMutLgPhyyl3s0wAbin0/EFyYwYqA63i6Y3Oro1FK1XH6pnwDVrTxUxwmiPD4y7HpyMJKqWrSsbwaKocdk+zq7rpcu7uUUl6gCaWh2ruK4OIjrA0ZQnxshNXRKKXqAe3yaqDyf54LJoioPqPr74RhSlXTTYtuAhrGeH7eoC2Uhshhx++XBSxznsvovh2tjkapBqN0+PoNGzZwwQUX0KNHD+Lj4/n4449PK+uN4esB1q9fz6JFi7wS/9loC6WheedyKMiiUUkWyZHDGNM85OzHKKW8qnHjxrz33nt07tyZAwcO0LdvX0aMGEGTJk2Ol/F0+PqzWb9+PVu2bGHkyJFeif1MtIXSAGXl5pBngmmd+DurQ1GqQerSpQudO7uGOmrVqhUtWrSgMkND7dixgxEjRtC3b18GDx7Mr7/+CsCcOXPo2bMnCQkJDB06lIKCAp544gk+/PDDKrVuKktbKA2NcRJUkM5Xzr5c1qe91dEoZYnpa6bzy5FfzlqutEzpvZQzOafpOTzYr8JhBSu0Zs0aiouL6djR8+7nyZMnM2vWLDp27MiqVau46667WLx4MVOnTmX58uW0bNmSrKwsGjVqxGOPPXZ8ThRf04TSwJiCbBpRyG8tL2VcWNDZD1BK+UxaWhrXXXcds2fPxs/Psw6jrKwsVq9efdJQK3a7HYABAwZw/fXXc9VVVzFhwgSfxHwmmlAamD0ZWTQzjWjfX7u7VMPlaUvCl0955eTkcPnll/PUU09x/vnne3ycMYbmzZuXe0/lzTff5Mcff2ThwoUkJCSwadMmb4Z8VnoPpSEoKYRtC2HuzcSag3zt7MPw+HZWR6VUg1VcXMz48eOPtyYqIzIykpiYGJKSkgBwOp1s3LgRgN27d3P++efz5JNPEhkZSWpqKmFhYeTm5nq9DuXRhFJf2Yth+yKYNxnzfEf4+Fryti1lrmMwy8x5hAZp41Qpq3zyySesWLGCd9999/jjwJV5imvOnDm88cYbJCQk0KNHDxYuXAjAPffcQ69evejVqxfDhw+nZ8+eXHzxxWzcuJE+ffroTXlVCY4S+O1b2JKE2fY5UpRNvl8Y/7Mn8pn9fLYG9aaPbOC6oBVWR6pUg1Q6ZPykSZOYNGmSR8eUN3x9hw4dyp0/ZcGCBadti4qKYt26dVWItvI0odR1Djvs/Q62zMO59XP8Co9wTBqzyN6XBY7z2RmSyLCEWO7oEU2/9k3ZPv1pqyNWqs7QN+QrRxNKXeR0wL4fIDkJx5b52AoOUyDBLLafy0LH+eyNvICLe7blLz2jiW8dgV+ZkYR7xOi4XUop39CEUlc4nZCyFrPlU+xbkgg4lk4hgSx19GGhYxLpLQdyUc84HugZTecWoTo+l1KqxmlCqc2MgQPrMZvnUbJ5HoH5BygmgOWOBBY6J5LV+mKG9GrPIz2iadO0sdXRKqUaOE0otY0xcHATjs2fUrJpHsF5+7HjzwpHL/5nxpEbdykX9erI37q3oEVYcOXPr1OaKqV8RBNKbWAMpG/FvulTijZ+SkjeHpzYWO3oyWK5nMIOlzE4oTOPndOCiEYBVkerVIOx97rrAWj3/nsWR1I3aEKxUsavFG78LyUb5xKWuxsxws/O7iyzTaak82gG9e7K3zpH0SjQZnWkSikvCA0NJS8vjw0bNnDHHXeQk5ODzWbjkUce4eqrrz6p7J133smqVasoLi7mt99+o2vXrgA8+uijXHnllR5dLykpiZ07d/LAAw94vS7l0YRS0zJ3kf/zfyne+CmRub8SaIQN5hy+9f8jzm6/Y2Dv7jzcoRkBNn3nVKn6ypvD19vtdvz9y/9RPn78eO8HfwaaUGrC0b1krfsE+6ZPaZ67jRBgm7MLq4JuRXqMZUCfnjzQJvKkx3uVUvVXly5dji+XHb6+bEI5k4EDBzJkyBBWrlzJhAkTaN++Pc888wzFxcVERUXxwQcf0KJFC2bNmnV8pOFJkybRrFkz1q5dy8GDB3nxxRe9nnA0ofiIydrP4TWf4Nj8KdG5yTQBNjg7Mq/xzfj1HM+Avr35c3SYPt6rlAUOPvMMRdvOPnx94S+uMqX3Us4kqNs5RD/8cKVjqcrw9eAaXHLFCteoF0ePHmXMmDGICG+88QYvvvgi06dPP+2Y9PR0Vq1axebNm5k4caImlNrM5KRx4Ps5mOR5xOZuIgrY4oxjSdhN2HpN4MLEvkzWGRKVUm5VGb6+1DXXXHN8ed++fUycOJGDBw9SVFR0UguorHHjxiEixMfHk5qaWq3Yy6MJpZrsOYfY990c/LYm0TZvA60x/OJsw3+b3EBA/BVc0K8/14VX4fFepZTPeNqS8OVTXlUdvr5USMiJX07vvPNOHn74YUaNGsXSpUuZNm1auccEBZ2YA8kYU/mgz0ITShUUZmfw23dzsG2dT8f8n+iAYZdpxcLI6wjufQX9+l3IOY0DrQ5TKVVLVWf4+vJkZ2fTunVrjDHMnj3bCxFWjSYUD+VlZ7JzxRwCts2nS/5PdBMHe000S5tfS0ifqzj3vAF0DNJ3RJRSZ1c6fH1mZibvvvsuwPGh7Kvi8ccfZ/z48cTGxtKvXz/S0tK8GK3nxBfNntoqMTHRVGUY5x9fvZ4+h78gUOyk0oKdUcMJOXci8YmDCQzQd0SUqgu2bdtGt27dKnVMQ3yxsbw/JxH5yRiTeLZjtYXiAWd4G9bbriQscSLn9B1Ka31HRKkGoSElEm/QhOKBC27QOUSUUupsLPlVW0SaisgSEdnh/o4sp8xQEdlQ5lMoIuPc+9qLyI/u4z8WEb0DrpRSFrOq72YKsMwY0xlY5l4/iTHmG2NMb2NMb+Bi4Biw2L17OvCy+/ijwC01E7ZSqi5rSPeMq6K6fz5WJZSxQOmzbbOBcWcpfyXwP2PMMXG9Wn4xMLcSxyulGrjg4GAyMzM1qVTAGENmZibBwVV/b86qeygtjTFpAMaYNBFpcZby1wAvuZebAVnGGLt7PQVoXdGBIjIZmAzQtm3bagWtlKq7YmNjSUlJISMjw+pQaq3g4GBiY2OrfLzPEoqILAWiy9n1SCXPEwP0Ar4q3VROsQp/5TDGzARmguux4cpcWylVfwQEBNC+fXurw6jXfJZQjDHDK9onIodEJMbdOokB0s9wqolAkjGmxL1+GGgiIv7uVkoscMBrgSullKoSq+6hLABucC/fAHx2hrK/Bz4qXTGuDtBvcN1X8eR4pZRSNcCqhDINuEREdgCXuNcRkUQRmVVaSETigDbAt6cc/yBwr4jsxHVP5a0aiFkppdQZNKihV0QkA9hbxcOb4+puqw/qS13qSz1A61Jb1Ze6VLce7YwxUWcr1KASSnWIyDpPxrKpC+pLXepLPUDrUlvVl7rUVD10UCqllFJeoQlFKaWUV2hC8dxMqwPwovpSl/pSD9C61Fb1pS41Ug+9h6KUUsortIWilFLKKzShVIKIPCkim9zD6S8WkVZWx1RVIvK8iPzirk+SiDSxOqaqEJGrRCRZRJwiUiefxhGRkSKyXUR2ishpI2/XFSLytoiki8gWq2OpDhFpIyLfiMg299+tu62OqapEJFhE1ojIRnddpvr0etrl5TkRCTfG5LiX/wx0N8bcbnFYVSIilwJfG2PsIjIdwBjzoMVhVZqIdAOcwAzgfmNM5ed4tpCI2IBfcb3gmwKsBX5vjNlqaWBVICKDgTzgPWNMT6vjqSr3cFAxxpj1IhIG/ASMq6P/TQQIMcbkiUgA8B1wtzFmtS+upy2USihNJm4hnGFQytrOGLO4zIjNq3GNiVbnGGO2GWO2Wx1HNfQDdhpjdhtjioE5uKZ3qHOMMSuAI1bHUV3GmDRjzHr3ci6wjTOMaF6bGZc892qA++Ozn1uaUCpJRJ4Wkf3AtcBjVsfjJTcD/7M6iAaqNbC/zPoZp2NQNcs9/FMf4EdrI6k6EbGJyAZcg/AuMcb4rC6aUE4hIktFZEs5n7EAxphHjDFtgA+Bu6yN9szOVhd3mUcAO6761Eqe1KMOq9R0DKrmiEgo8Cnwl1N6J+oUY4zDPfNtLNBPRHzWHWnVBFu11pmG3T/Ff4AvgL/7MJxqOVtdROQGYDQwzNTim2mV+G9SF6XgGgC1lE7HUAu47zd8CnxojJlndTzeYIzJEpHlwEjAJw9OaAulEkSkc5nVMcAvVsVSXSIyEteozWOMMcesjqcBWwt0FpH2IhKIa3bSBRbH1KC5b2S/BWwzxrx0tvK1mYhElT7BKSKNgOH48OeWPuVVCSLyKdAV11NFe4HbjTGp1kZVNe6h/4OATPem1XXxiTURGQ+8CkQBWcAGY8wIa6OqHBEZBbwC2IC3jTFPWxxSlYjIR8BFuEa2PQT83RhT56aWEJGBwEpgM65/6wAPG2O+tC6qqhGReGA2rr9bfsAnxpgnfHY9TShKKaW8Qbu8lFJKeYUmFKWUUl6hCUUppZRXaEJRSinlFZpQlFJKeYUmFKW8SETyzl7qjMfPFZEO7uVQEZkhIrvcI8WuEJH+IhLoXtYXk1WtoglFqVpCRHoANmPMbvemWbgGW+xsjOkB3Ag0dw8iuQy42pJAlaqAJhSlfEBcnnePObZZRK52b/cTkX+7WxwLReRLEbnSfdi1wGfuch2B/sCjxhgngHtE4i/cZee7yytVa2iTWSnfmAD0BhJwvTm+VkRWAAOAOKAX0ALX0Ohvu48ZAHzkXu6B661/RwXn3wKc55PIlaoibaEo5RsDgY/cI70eAr7FlQAGAv81xjiNMQeBb8ocEwNkeHJyd6Ipdk8ApVStoAlFKd8ob1j6M20HKACC3cvJQIKInOnfaBBQWIXYlPIJTShK+cYK4Gr35EZRwGBgDa4pWK9w30tpiWswxVLbgE4AxphdwDpgqnv0W0Skc+kcMCLSDMgwxpTUVIWUOhtNKEr5RhKwCdgIfA381d3F9SmuOVC2ADNwzQSY7T7mC05OMLcC0cBOEdkMvMmJuVKGAnVu9FtVv+low0rVMBEJNcbkuVsZa4ABxpiD7vkqvnGvV3QzvvQc84CHjDHbayBkpTyiT3kpVfMWuic9CgSedLdcMMYUiMjfcc0pv6+ig90Tcc3XZKJqG22hKKWU8gq9h6KUUsorNKEopZTyCk0oSimlvEITilJKKa/QhKKUUsorNKEopZTyiv8HR1rTh3wp4rcAAAAASUVORK5CYII=\n”,
“text/plain”: [
“
]
},
“metadata”: {
“needs_background”: “light”
},
“output_type”: “display_data”
}
],
“source”: [
“test_means = grid.cv_results_[ ‘mean_test_score’ ]\n”,
“test_stds = grid.cv_results_[ ‘std_test_score’ ]\n”,
“train_means = grid.cv_results_[ ‘mean_train_score’ ]\n”,
“train_stds = grid.cv_results_[ ‘std_train_score’ ]\n”,
“\n”,
“\n”,
“n_Cs = len(Cs)\n”,
“number_penaltys = len(penaltys)\n”,
“test_scores = numpy.array(test_means).reshape(n_Cs,number_penaltys)\n”,
“train_scores = numpy.array(train_means).reshape(n_Cs,number_penaltys)\n”,
“test_stds = numpy.array(test_stds).reshape(n_Cs,number_penaltys)\n”,
“train_stds = numpy.array(train_stds).reshape(n_Cs,number_penaltys)\n”,
“\n”,
“x_axis = numpy.log10(Cs)\n”,
“for i, value in enumerate(penaltys):\n”,
” #pyplot.plot(log(Cs), test_scores[i], label= ‘penalty:’ + str(value))\n”,
” matplot.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +’ Test’)\n”,
” matplot.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +’ Train’)\n”,
” \n”,
“matplot.legend()\n”,
“matplot.xlabel( ‘log(C)’ ) \n”,
“matplot.ylabel( ‘neg-logloss’ )\n”,
“matplot.savefig(‘LogisticGridSearchCV_C.png’ )\n”,
“\n”,
“matplot.show()”
]
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“从图标中可以看出，参数C=1时，负损失最小”
]
},
{
“cell_type”: “code”,
“execution_count”: 32,
“metadata”: {},
“outputs”: [
{
“name”: “stdout”,
“output_type”: “stream”,
“text”: [
“(0.7337662337662337, 0.7402597402597403)\n”
]
}
],
“source”: [
“lr_l1= LogisticRegression(penalty=’l1′,C=1)\n”,
“lr_l1.fit(x_train,y_train)\n”,
“accuracy_l1 = lr_l1.score(x_test, y_test)\n”,
“lr_l2= LogisticRegression(penalty=’l2′,C=1)\n”,
“lr_l2.fit(x_train,y_train)\n”,
“accuracy_l2 = lr_l2.score(x_test, y_test)\n”,
“print(accuracy_l1,accuracy_l2)”
]
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“发现带入参数后，L2正则比L1正则性能更好”
]
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“### Default SVC”
]
},
{
“cell_type”: “code”,
“execution_count”: 14,
“metadata”: {},
“outputs”: [],
“source”: [
“from sklearn.svm import LinearSVC\n”,
“import sklearn.metrics as metrics\n”,
“SVC1 = LinearSVC().fit(x_train, y_train)”
]
},
{
“cell_type”: “code”,
“execution_count”: 15,
“metadata”: {
“scrolled”: true
},
“outputs”: [
{
“name”: “stdout”,
“output_type”: “stream”,
“text”: [
“Classification report for classifier LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n”,
” intercept_scaling=1, loss=’squared_hinge’, max_iter=1000,\n”,
” multi_class=’ovr’, penalty=’l2′, random_state=None, tol=0.0001,\n”,
” verbose=0):\n”,
” precision recall f1-score support\n”,
“\n”,
” 0 0.75 0.90 0.82 99\n”,
” 1 0.72 0.47 0.57 55\n”,
“\n”,
“avg / total 0.74 0.75 0.73 154\n”,
“\n”,
“\n”,
“Confusion matrix:\n”,
“[[89 10]\n”,
” [29 26]]\n”
]
}
],
“source”: [
“y_predict = SVC1.predict(x_test)\n”,
“\n”,
“print(\”Classification report for classifier %s:\\n%s\\n\”% (SVC1, metrics.classification_report(y_test, y_predict)))\n”,
“print(\”Confusion matrix:\\n%s\” % metrics.confusion_matrix(y_test, y_predict))”
]
},
{
“cell_type”: “code”,
“execution_count”: 82,
“metadata”: {},
“outputs”: [],
“source”: [
“def fit_grid_point_Linear(C, x_train, y_train, x_val, y_val):\n”,
” \n”,
” # 在训练集是那个利用SVC训练\n”,
” SVC2 = LinearSVC( C = C)\n”,
” SVC2 = SVC2.fit(x_train, y_train)\n”,
” \n”,
” # 在校验集上返回accuracy\n”,
” accuracy = SVC2.score(x_val, y_val)\n”,
” \n”,
” print(\”accuracy: {}\”.format(accuracy))\n”,
” return accuracy”
]
},
{
“cell_type”: “code”,
“execution_count”: 86,
“metadata”: {},
“outputs”: [
{
“name”: “stdout”,
“output_type”: “stream”,
“text”: [
“accuracy: 0.727272727273\n”,
“accuracy: 0.733766233766\n”,
“accuracy: 0.74025974026\n”,
“accuracy: 0.733766233766\n”,
“accuracy: 0.753246753247\n”,
“accuracy: 0.701298701299\n”,
“accuracy: 0.701298701299\n”
]
}
],
“source”: [
“C_s = numpy.logspace(-3, 3, 7)\n”,
“accuracy_s = []\n”,
“for i, oneC in enumerate(C_s):\n”,
“# for j, penalty in enumerate(penalty_s):\n”,
” tmp = fit_grid_point_Linear(oneC, x_train, y_train, x_test, y_test)\n”,
” accuracy_s.append(tmp)”
]
},
{
“cell_type”: “code”,
“execution_count”: 87,
“metadata”: {},
“outputs”: [
{
“data”: {
“image/png”: 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\n”,
“text/plain”: [
“
]
},
“metadata”: {
“needs_background”: “light”
},
“output_type”: “display_data”
}
],
“source”: [
“x_axis = numpy.log10(C_s)\n”,
“\n”,
“matplot.plot(x_axis, numpy.array(accuracy_s), ‘b-‘)\n”,
” \n”,
“matplot.legend()\n”,
“matplot.xlabel( ‘log(C)’ ) \n”,
“matplot.ylabel( ‘accuracy’ )\n”,
“matplot.savefig(‘SVM_Otto.png’ )\n”,
“\n”,
“matplot.show()”
]
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“可以由图看吃，C的取值为1时，准确度比较高\n”,
“\n”,
“### RBF核”
]
},
{
“cell_type”: “code”,
“execution_count”: 88,
“metadata”: {},
“outputs”: [],
“source”: [
“def fit_grid_point_RBF(C, gamma, x_train, y_train, x_val, y_val):\n”,
” \n”,
” # 在训练集是那个利用SVC训练\n”,
” SVC3 = SVC( C = C, kernel=’rbf’, gamma = gamma)\n”,
” SVC3 = SVC3.fit(x_train, y_train)\n”,
” \n”,
” # 在校验集上返回accuracy\n”,
” accuracy = SVC3.score(x_val, y_val)\n”,
” \n”,
” print(\”accuracy: {}\”.format(accuracy))\n”,
” return accuracy”
]
},
{
“cell_type”: “code”,
“execution_count”: 89,
“metadata”: {},
“outputs”: [
{
“name”: “stdout”,
“output_type”: “stream”,
“text”: [
“accuracy: 0.642857142857\n”,
“accuracy: 0.642857142857\n”,
“accuracy: 0.642857142857\n”,
“accuracy: 0.642857142857\n”,
“accuracy: 0.642857142857\n”,
“accuracy: 0.642857142857\n”,
“accuracy: 0.707792207792\n”,
“accuracy: 0.642857142857\n”,
“accuracy: 0.642857142857\n”,
“accuracy: 0.642857142857\n”,
“accuracy: 0.733766233766\n”,
“accuracy: 0.74025974026\n”,
“accuracy: 0.707792207792\n”,
“accuracy: 0.642857142857\n”,
“accuracy: 0.642857142857\n”,
“accuracy: 0.753246753247\n”,
“accuracy: 0.727272727273\n”,
“accuracy: 0.688311688312\n”,
“accuracy: 0.642857142857\n”,
“accuracy: 0.642857142857\n”,
“accuracy: 0.746753246753\n”,
“accuracy: 0.662337662338\n”,
“accuracy: 0.688311688312\n”,
“accuracy: 0.642857142857\n”,
“accuracy: 0.642857142857\n”
]
}
],
“source”: [
“from sklearn.svm import SVC\n”,
“C_s = numpy.logspace(-2, 2, 5)\n”,
“gamma_s = numpy.logspace(-2, 2, 5) \n”,
“\n”,
“accuracy_s = []\n”,
“for i, oneC in enumerate(C_s):\n”,
” for j, gamma in enumerate(gamma_s):\n”,
” tmp = fit_grid_point_RBF(oneC, gamma, x_train, y_train, x_test, y_test)\n”,
” accuracy_s.append(tmp)”
]
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“### 从结果上看，gamma值不同准确度也不同，最高在0.75”
]
},
{
“cell_type”: “code”,
“execution_count”: 90,
“metadata”: {},
“outputs”: [
{
“data”: {
“image/png”: 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\n”,
“text/plain”: [
“
]
},
“metadata”: {
“needs_background”: “light”
},
“output_type”: “display_data”
}
],
“source”: [
“accuracy_s1 =numpy.array(accuracy_s).reshape(len(C_s),len(gamma_s))\n”,
“x_axis = numpy.log10(C_s)\n”,
“for j, gamma in enumerate(gamma_s):\n”,
” matplot.plot(x_axis, numpy.array(accuracy_s1[:,j]), label = ‘ Test – log(gamma)’ + str(numpy.log10(gamma)))\n”,
“\n”,
“matplot.legend()\n”,
“matplot.xlabel( ‘log(C)’ ) \n”,
“matplot.ylabel( ‘accuracy’ )\n”,
“matplot.savefig(‘RBF_SVM_Otto.png’ )\n”,
“\n”,
“matplot.show()”
]
},
{
“cell_type”: “markdown”,
“metadata”: {},
“source”: [
“### 由RBF核看出，参数取值为1时测试结果相对比较稳定。如果c取值为10，则结果有时准确度会低于0.7,可能发生了过拟合\n”,
“\n”,
“并且根据结果准确度和负损失来看，SVM比Logistic回归更好。”
]
}
],
“metadata”: {
“kernelspec”: {
“display_name”: “Python 2”,
“language”: “python”,
“name”: “python2”
},
“language_info”: {
“codemirror_mode”: {
“name”: “ipython”,
“version”: 2
},
“file_extension”: “.py”,
“mimetype”: “text/x-python”,
“name”: “python”,
“nbconvert_exporter”: “python”,
“pygments_lexer”: “ipython2”,
“version”: “2.7.15”
}
},
“nbformat”: 4,
“nbformat_minor”: 2
}
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发布者：全栈程序员-用户IM，转载请注明出处：https://javaforall.cn/160869.html原文链接：https://javaforall.cn
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