diff --git a/README.md b/README.md index d1c6f16..c0950b9 100644 --- a/README.md +++ b/README.md @@ -42,15 +42,16 @@ class CollaborationFramework: | 2 | 9/14 | ✅ | | | | | 3 | 9/21 | ✅ | | | | | 4 | 9/28 | ✅ | | 권명석 | | -| 5 | 10/5 | ✅ | 강민석(가족여행_비행기 탑승) | 김정현 | | +| 5 | 10/5 | ✅ | | 김정현 | | | 6 | 10/12 | ✅ | | 조해창 | 강산아 | -| 7 | 10/19 | ✅ | | 장성은 | 강민석 | +| 7 | 10/19 | ✅ | | 장성은 | | | 8 | 10/26 | | — | — | — | | 9 | 11/2 | ✅ | 장성은 (학회) | 김시은 | 전소영 | | 10 | 11/9 | ✅ | 권명석, 강산아, 장성은 (학회) | | | | 11 | 11/16 | | — | — | — | | 12 | 11/23 | ✅ | | 조해창 | 김시은 | | 13 | 11/30 | ✅ | | 김정현 | 강산아 | -| 14 | 12/7 | ✅ | | 전소영 | 강민석 | +| 14 | 12/7 | ✅ | | 전소영 | | | 15 | 12/14 | ✅ | 전소영 (학회) | 장성은 | | | 16 | 12/21 | ✅ | | — | — | + diff --git a/book/ate/data/learning_mindset.csv b/book/ate/data/learning_mindset.csv deleted file mode 100644 index 16392c9..0000000 --- a/book/ate/data/learning_mindset.csv +++ /dev/null @@ -1,10392 +0,0 @@ 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a/book/ate/propensity_score_and_dml.ipynb b/book/ate/propensity_score_and_dml.ipynb index 07f6103..c2e7639 100644 --- a/book/ate/propensity_score_and_dml.ipynb +++ b/book/ate/propensity_score_and_dml.ipynb @@ -11,7 +11,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "- Matching" + "- Matching\n", + "- IPW, AIPW, Doubly Robust Estimator\n", + "- Double Machine Learning (비모수 버전의 Regression 처럼 활용 가능)" ] }, { @@ -27,2558 +29,13 @@ " async>\n", "" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- IPW, AIPW, Doubly Robust Estimator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "출처: https://matheusfacure.github.io/python-causality-handbook/11-Propensity-Score.html" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "from causalinference import CausalModel" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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schoolidinterventionachievement_scoresuccess_expectethnicitygenderfrst_in_familyschool_urbanicityschool_mindsetschool_achievementschool_ethnic_minorityschool_povertyschool_size
2597311.48082851201-0.4629450.652608-0.515202-0.1698490.173954
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\n", - "
" - ], - "text/plain": [ - " schoolid intervention achievement_score success_expect ethnicity \\\n", - "259 73 1 1.480828 5 1 \n", - "3435 76 0 -0.987277 5 13 \n", - "9963 4 0 -0.152340 5 2 \n", - "4488 67 0 0.358336 6 14 \n", - "2637 16 1 1.360920 6 4 \n", - "\n", - " gender frst_in_family school_urbanicity school_mindset \\\n", - "259 2 0 1 -0.462945 \n", - "3435 1 1 4 0.334544 \n", - "9963 2 1 0 -2.289636 \n", - "4488 1 0 4 -1.115337 \n", - "2637 1 0 1 -0.538975 \n", - "\n", - " school_achievement school_ethnic_minority school_poverty school_size \n", - "259 0.652608 -0.515202 -0.169849 0.173954 \n", - "3435 0.648586 -1.310927 0.224077 -0.426757 \n", - "9963 0.190797 0.875012 -0.724801 0.761781 \n", - "4488 1.053089 0.315755 0.054586 1.862187 \n", - "2637 1.433826 -0.033161 -0.982274 1.591641 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = pd.read_csv(\"./data/learning_mindset.csv\")\n", - "data.sample(5, random_state=5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Propensity Score 계산" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "categ = [\"ethnicity\", \"gender\", \"school_urbanicity\"]\n", - "cont = [\"school_mindset\", \"school_achievement\", \"school_ethnic_minority\", \"school_poverty\", \"school_size\"]\n", - "\n", - "data_with_categ = pd.concat([\n", - " data.drop(columns=categ), \n", - " pd.get_dummies(data[categ], columns=categ, drop_first=False)\n", - "], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.linear_model import LogisticRegression\n", - "\n", - "T = 'intervention'\n", - "Y = 'achievement_score'\n", - "X = data_with_categ.columns.drop(['schoolid', T, Y])\n", - "\n", - "ps_model = LogisticRegression(C=1e6).fit(data_with_categ[X], data_with_categ[T])\n", - "\n", - "data_ps = data.assign(propensity_score=ps_model.predict_proba(data_with_categ[X])[:, 1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## IPW 생성 및 ATE 추정" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from sklearn.linear_model import LogisticRegression, Ridge\n", - "from causallib.estimation.standardization import Standardization\n", - "from causallib.estimation.ipw import IPW\n", - "from causallib.estimation.doubly_robust import AIPW" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Vanilla IPW" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Original Sample Size 10391\n", - "Treated Population Sample Size 10387.611484606225\n", - "Untreated Population Sample Size 10391.506082909109\n" - ] - } - ], - "source": [ - "weight_t = 1/data_ps.query(\"intervention==1\")[\"propensity_score\"]\n", - "weight_nt = 1/(1-data_ps.query(\"intervention==0\")[\"propensity_score\"])\n", - "print(\"Original Sample Size\", data.shape[0])\n", - "print(\"Treated Population Sample Size\", sum(weight_t))\n", - "print(\"Untreated Population Sample Size\", sum(weight_nt))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Y1: 0.25981028516541704\n", - "Y0: -0.12903052853557984\n", - "ATE 0.38884081370099727\n" - ] - } - ], - "source": [ - "weight = ((data_ps[\"intervention\"]-data_ps[\"propensity_score\"]) /\n", - " (data_ps[\"propensity_score\"]*(1-data_ps[\"propensity_score\"])))\n", - "\n", - "y1_ipw = sum(data_ps.query(\"intervention==1\")[\"achievement_score\"]*weight_t) / len(data)\n", - "y0_ipw = sum(data_ps.query(\"intervention==0\")[\"achievement_score\"]*weight_nt) / len(data)\n", - "\n", - "ate_ipw = y1_ipw - y0_ipw\n", - "#ate = np.mean(weight * data_ps[\"achievement_score\"])\n", - "\n", - "print(\"Y1:\", y1_ipw)\n", - "print(\"Y0:\", y0_ipw)\n", - "print(\"ATE\", np.mean(weight * data_ps[\"achievement_score\"]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "결과 해석: \n", - "1. Treatment 받은 개인이 Treatment 받지 않은 동료보다 achievement_score가 0.38 표준편차 더 크다. (achievement_score는 표준화된 결과이기 때문에 표준 편차의 차이로 해석)\n", - "2. 아무도 Treatment 받지 않은 경우 일반적인 성취 수준이 현재보다 0.12 표준편차 더 낮다.\n", - "3. 모든 사람이 Treatment(세미나)를 받았다면 일반적인 성취 수준이 0.25 표준편차 더 높음." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "또한 ate를 나타내는 하나의 코드가 더 있다. \n", - "위의 코드에 주석처리한 부분을 그대로 실행해보면 똑같은 결과를 얻을 수 있는 것을 알 수 있다." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.38884081370099727\n" - ] - } - ], - "source": [ - "ate_ipw = np.mean(weight * data_ps[\"achievement_score\"])\n", - "print(ate_ipw)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "두 결과값이 같은 이유는 Matheus Facure(출처)의 책에서 자세히 설명되어 있다. \n", - "(참고: $ \\mathrm{ATE}=\\mathbb{E}\\!\\left[\\, Y\\,\\dfrac{T-e(X)}{e(X)\\,\\bigl(1-e(X)\\bigr)} \\right] $)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Doubly Robust Estimator & AIPW" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "출처: https://causallib.readthedocs.io/en/latest/causallib.estimation.doubly_robust.html?highlight=doubly" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import KFold\n", - "from causallib.estimation.ipw import IPW\n", - "from causallib.estimation.doubly_robust import AIPW\n", - "from causallib.estimation.standardization import Standardization\n", - "from sklearn.linear_model import LogisticRegression, LinearRegression, Ridge" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "Y = data[\"achievement_score\"]\n", - "T = data[\"intervention\"]\n", - "X = pd.get_dummies(\n", - " data[[\"school_mindset\",\"school_achievement\",\"school_ethnic_minority\",\n", - " \"school_poverty\",\"school_size\",\"ethnicity\",\"gender\",\"school_urbanicity\"]],\n", - " drop_first=False\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "DR Estimator는 결과모형과 IPW값이 모두 필요함 \n", - "- Y값(achievement_score)을 Ridge로 예측(L2 패널티 부여) \n", - "- IPW: 로지스틱 회귀 사용" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "outcome_model = Standardization(learner=Ridge(alpha=1.0))\n", - "weight_model = IPW(learner=LogisticRegression(max_iter=1000),\n", - " clip_min=0.01, clip_max=0.99, use_stabilized=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PS Score를 구할 때 max_iter을 충분히 큰 숫자(1000)으로 설정해 수치 최적화가 수렴할 수 있도록 설정합니다. \n", - "또한 클리핑을 사용하여 $ \\hat{e} $가 [0.01, 0.99]에서만 존재하도록 극단 가중치를 완화합니다. \n", - "(use_stabilized = True) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "위에서 구한 PS score과 IPW를 활용하여 AIPW를 구합니다. (Vanilla)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
-       "                learner=Ridge()), overlap_weighting=False, predict_proba=False, weight_covariates=None,\n",
-       "     weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
-       "    learner=LogisticRegression(max_iter=1000)))
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" - ], - "text/plain": [ - "AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n", - " learner=Ridge()), overlap_weighting=False, predict_proba=False, weight_covariates=None,\n", - " weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n", - " learner=LogisticRegression(max_iter=1000)))" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dr = AIPW(outcome_model=outcome_model, weight_model=weight_model, overlap_weighting=False)\n", - "dr.fit(X, T, Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "μ1 (A=1): 0.3032993079280466\n", - "μ0 (A=0): -0.14711303868200887\n", - "ATE (DR, vanilla): 0.45041234661005547\n" - ] - } - ], - "source": [ - "pop_outcomes = dr.estimate_population_outcome(X, T, Y)\n", - "mu1_aipw, mu0_aipw = pop_outcomes[1], pop_outcomes[0]\n", - "ate_aipw = dr.estimate_effect(mu1_aipw, mu0_aipw, agg=\"population\")[\"diff\"]\n", - "print(\"μ1 (A=1):\", mu1_aipw)\n", - "print(\"μ0 (A=0):\", mu0_aipw)\n", - "print(\"ATE (DR, vanilla):\", ate_aipw)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "결과 해석은 IPW에서와 마찬가지로 생각하면 됩니다. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "위에서 구한 AIPW는 Propensity Score의 Overlap이 충분히 확보되었을 때는 좋은 결과를 나타냅니다. \n", - "하지만 Overlap 구간이 불안정할 때는 Overlap-weighting = True이라는 기능을 활용해도 좋습니다." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
-       "                learner=Ridge()), overlap_weighting=True, predict_proba=False, weight_covariates=None,\n",
-       "     weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
-       "    learner=LogisticRegression(max_iter=1000)))
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" - ], - "text/plain": [ - "AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n", - " learner=Ridge()), overlap_weighting=True, predict_proba=False, weight_covariates=None,\n", - " weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n", - " learner=LogisticRegression(max_iter=1000)))" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dr_overlap = AIPW(outcome_model=outcome_model,\n", - " weight_model=weight_model,\n", - " overlap_weighting=True)\n", - "dr_overlap.fit(X, T, Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ATE(DR, overlap-weighting): 0.34674117821712797\n" - ] - } - ], - "source": [ - "pop_outcomes_ov = dr_overlap.estimate_population_outcome(X, T, Y)\n", - "ate_ov = dr_overlap.estimate_effect(pop_outcomes_ov[1], pop_outcomes_ov[0],\n", - " agg=\"population\")[\"diff\"]\n", - "print(\" ATE(DR, overlap-weighting):\", ate_ov)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Summary" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " estimator μ1 μ0 ATE\n", - "0 IPW (manual) 0.259810 -0.129031 0.388841\n", - "1 AIPW (vanilla) 0.303299 -0.147113 0.450412\n", - "2 AIPW (overlap) NaN NaN 0.346741\n" - ] - } - ], - "source": [ - "results = pd.DataFrame([\n", - " [\"IPW (manual)\", y1_ipw, y0_ipw, ate_ipw],\n", - " [\"AIPW (vanilla)\", mu1_aipw, mu0_aipw, ate_aipw],\n", - " [\"AIPW (overlap)\", np.nan, np.nan, ate_ov]\n", - "], columns=[\"estimator\", \"μ1\", \"μ0\", \"ATE\"])\n", - "print(results)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "위에서 사용한 방법에 따라 ATE 값이 다르게 나타나는 것을 알 수 있습니다. \n", - " \n", - "하지만 Overlap-Weighting 옵션을 사용한 경우에는 ATE가 아닌 ATO를 추정한 것이고 둘을 직접 비교하는 것은 맞지 않을 수 있습니다. \n", - " \n", - "분석의 목적이 ATE를 추정하는 것인지 ATO를 추정하는 것인지 확인한 후 적절히 사용하면 됩니다. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## IPW Robustness Check의 흐름\n", - "\n", - "각각의 수치에 대한 검증은 필수입니다. \n", - "어떤 수치가 더 Robust하게 추정이 된 걸까요? \n", - "\n", - "먼저 IPW로 구한 ATE의 신빙성을 테스트 해보겠습니다." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "출처: https://causallib.readthedocs.io/en/latest/causallib.evaluation.plots.plots.html" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "from causallib.evaluation.plots.plots import plot_propensity_score_distribution\n", - "from causallib.evaluation import evaluate" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### IPW 가중 후 |SMD| 변화 확인" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - }, - { 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ARERERKRVWAATERERkVZhAUxEREREWoWjQBARERFRqRAEAVWqVJGeawoLYCIiIiIqFfr6+vDz89N0GuwCQURERETahQUwEREREWkVdoEgIiIiolIhk8mwcuVKAMDo0aOhr6+vkTxYABMRERFRqRBFEUlJSdJzTWEXCCIiIiLSKiyAiYiIiEirsAAmIiIiIq3CApiIiIiItAoLYCIiIiLSKiyAtZQgCNi5c2eJ7iMqKgqCIODly5fvxXaIKD+5QsTxW8/we+xDHL/1DHKF5q7KVgtRBB6eyf6XiN47giDAxsYGNjY2vBUy0Zs0b94cCQkJsLS01HQqROVKxKUEBO6OQ0JSujTP3tIIAV3c4FPfXoOZvYMLm4Edw4FuPwIen2k6GyLKQ19fH6NGjdJ0GmwBpvefgYEB7OzsNPpNkai8ibiUgJE/nVUqfgHgcVI6Rv50FhGXEjSU2TuQZwGHgrKfRwVlTxMRqcACuIzbunUr3N3dYWxsjIoVK6J9+/ZITU0FAKxbtw716tWDoaEh7O3tMWbMGKV1nz59im7dusHExASurq7YtWuX0vLo6Gh4enpK60+fPh1ZWf99oGRkZGDcuHGwtbWFkZERWrRogVOnThXrOO7evYsuXbqgQoUKMDU1Rb169bB3714A+btAtGnTBoIg5HvcuXMHAJCUlIRhw4bB1tYWFhYW+Oijj3D+/Pli5UVUHskVIgJ3xyF3JwE9yKEHOXT//Xferot4nZ6BzMxMpf/3AJCZmVngQyaTFTtWJpO9W2zsFmS+fJgd8OIOcGmbul4yIipn2AWiDEtISECfPn0QEhKCbt26ISUlBUeOHIEoiggNDcWkSZMwf/58dOrUCUlJSTh27JjS+oGBgQgJCcG3336L5cuXo1+/frh79y6sra3x8OFDdO7cGb6+vtiwYQOuXr0KPz8/GBkZYc6cOQCAqVOnYtu2bVi/fj2cnJwQEhICb29v3Lx5E9bW1kU6ltGjRyMzMxOHDx+Gqakp4uLiYGZmpjJ2+/btyMzMVFr38uXLqFy5MkRRxMcffwxra2vs3bsXlpaW+OGHH9CuXTtcv369wLwyMjKQkZEhTScnJxcpf6Ky5GT883wtvwOMzykHyYCQBccBAK6urujbt6+0aOHChfkK0hxOTk7w9fWVppcuXYq0tDSVsVWqVIGfn580vXLlSukOUXnZ2Ngo/Wy6evVqPHnyJH+gMA4B4ncAdLJbgev3AHT5UUf0vpDJZFi9ejUAwM/Pj7dCpqJLSEhAVlYWunfvDicnJwCAu7s7AGDevHmYPHkyxo8fL8V/8MEHSuv7+vqiT58+AICgoCAsX74cJ0+ehI+PD1atWgVHR0esWLECgiCgTp06ePToEaZNm4bZs2fj9evXCA0NRXh4ODp16gQg+wMpMjISa9euxZQpU4p0LPfu3UOPHj2k/F1cXAqMzV3ELl68GAcPHsSJEydgbGyMgwcP4uLFi0hMTIShoSGA7A/rnTt3YuvWrRg2bJjKbQYHByMwMLBIOROVVYkp6W8PKvMU/7UCsy8w0XtDFEXpy6smb4XMArgM8/DwQLt27eDu7g5vb2907NgRPXv2hEwmw6NHj9CuXbs3rt+gQQPpuampKczNzZGYmAgAuHLlCpo1a6bU79bLywuvXr3CgwcP8PLlS8hkMnh5eUnL9fX14enpiStXrhT5WMaNG4eRI0di//79aN++PXr06KGUnyp//vknpk+fjt27d6NWrVoAgDNnzuDVq1eoWLGiUuzr169x69atArc1Y8YMTJo0SZpOTk6Go6NjkY+DqCywNTfKN2/j60b55oUN8sSH1a2ho6PcW87f37/Abeftq5/7S/jbYkePHl3gB2LeWD8/v/9i5VnA915A0n1AqWMHW4GJSDW+I5Rhurq6iIyMRExMDPbv34/ly5dj5syZOHDgQKHWz/uzgyAIUCgUALK/leX9wMn5sBEEQel53pjiXKw2dOhQeHt7448//sD+/fsRHByMRYsWYezYsSrj4+Li8Pnnn2P+/Pno2LGjNF+hUMDe3h5RUVH51rGysipw/4aGhlKLMVF551ndGvaWRniclC6Vi1nQlZYLAOwsjdDctTJ0dfL/fzYwMCj0vooSW5SfQpViz28Hkm6riGIrMBGpxovgyjhBEODl5YXAwECcO3cOBgYGiIyMhLOzc6ELYVXc3NwQExOj1BoTExMDc3NzODg4oGbNmjAwMMDRo0el5TKZDKdPn0bdunWLtU9HR0eMGDEC27dvx+TJk6U+Qnk9e/YMXbp0Qffu3TFx4kSlZY0bN8bjx4+hp6eHmjVrKj0qVapUrLyIyhtdHQEBXdwAZBe7ueVMB3RxU1n8vnekkR8KylWHI0IQUT4sgMuwEydOICgoCKdPn8a9e/ewfft2PHnyBHXr1sWcOXOwaNEiLFu2DDdu3MDZs2exfPnyQm971KhRuH//PsaOHYurV6/i999/R0BAACZNmgQdHR2Ymppi5MiRmDJlCiIiIhAXFwc/Pz+kpaVhyJAhRT6WCRMmYN++fYiPj8fZs2dx8ODBAgvp7t27w9jYGHPmzMHjx4+lh1wuR/v27dGsWTN07doV+/btw507dxATE4NZs2bh9OnTRc6LqLzyqW+P0P6NYWep3B3CztIIof0bl51xgO8dB17ehXLXh9z+bQW+d7wUkyKi9x27QJRhFhYWOHz4MJYsWYLk5GQ4OTlh0aJF0kVp6enpWLx4Mfz9/VGpUiX07Nmz0Nt2cHDA3r17MWXKFHh4eMDa2hpDhgzBrFmzpJj58+dDoVBgwIABSElJQdOmTbFv3z5UqFChyMcil8sxevRoPHjwABYWFvDx8cHixYtVxh4+fBgA4OzsrDQ/Pj4ezs7O2Lt3L2bOnInBgwfjyZMnsLOzQ6tWrVC5cuUi50VUnvnUt0cHNzucjH+OxJR02JobwbO6ddlo+c3h6An0CgeyMguO0TPIjiMi+pcgavISPKL3VHJyMiwtLZGUlAQLCwtNp0NERFQuyGQyrFy5EkD2ha/qHgatsJ/fbAEmIiIiolKhr6+PCRMmaDoN9gGm0tGpUyeYmZmpfAQFBWk6PSIiItIibAGmUrFmzRq8fv1a5bKi3jWOiIiI6F2wAKZS4eDgoOkUiIiISMNkMhnCw8MBZN+RlrdCJiIiIqJyTRRFPHr0SHquKewDTERERERahQUwEREREWkVFsBEREREpFVYABMRERGRVmEBTERERERahaNAEBEREVGpMTEx0XQKEERNjkFB9J4q7L3EiYiI6P1R2M9vdoEgIiIiIq3CApiIiIiItAr7ABMRERFRqZDJZNi0aRMAoF+/frwVMhERERGVb6Io4u7du9JzTWEXCCIiIiLSKiyAiYiIiEirsAAmIiIiIq3CApiIiIiItAoLYCIiIiLSKhwFgoiIiIhKjaaGPsuNt0ImUoG3QiYiIip7eCtkIiIiIiIVWAATERERkVZhH2AiIiIiKhVZWVnYsmULAKB3797Q09NMKcoCmIiIiIhKhUKhwI0bN6TnmsIuEERERESkVVgAExEREZFWYQFMRERERFqFBTARERERaRUWwERERESkVVgAExEREZFW4a2QiVTgrZCJiIjKHt4KmYiIiIhIBRbARERERKRVWAC/ZwRBwM6dO0t0H1FRURAEAS9fvizR/RBRyZErRBy/9Qy/xz7E8VvPIFeUg95sogg8PJP9LxGVS1lZWfjtt9/w22+/ISsrS2N58FbIVKbNmTMHO3fuRGxsrKZTISo1EZcSELg7DglJ6dI8e0sjBHRxg099ew1m9o4ubAZ2DAe6/Qh4fKbpbIioBCgUCsTFxQEAPv30U43lwRZgKpNEUdToN0ciTYm4lICRP51VKn4B4HFSOkb+dBYRlxI0lNk7kmcBh4Kyn0cFZU8TEZUQFsAlZOvWrXB3d4exsTEqVqyI9u3bIzU1FQCwbt061KtXD4aGhrC3t8eYMWOU1n369Cm6desGExMTuLq6YteuXUrLo6Oj4enpKa0/ffp0pWIwIyMD48aNg62tLYyMjNCiRQucOnWqWMcRHh4OKysr7Ny5E7Vq1YKRkRE6dOiA+/fvK8WFhoaiRo0aMDAwQO3atbFx40ZpWZ8+ffD5558rxctkMlSqVAlhYWEAsgvakJAQuLi4wNjYGB4eHti6dasUn9NtY9++fWjatCkMDQ2xceNGBAYG4vz58xAEAYIgIDw8HIMHD8Ynn3yitL+srCzY2dlh3bp1xXodiN4HcoWIwN1xyN1BQA9y6EEO3X//nbfrIl6nZyAzMzPfl8TMzMwCHzKZrNixMpns3WNjt0D28mF20Is7wKVt6nrZiIjyYReIEpCQkIA+ffogJCQE3bp1Q0pKCo4cOQJRFBEaGopJkyZh/vz56NSpE5KSknDs2DGl9QMDAxESEoJvv/0Wy5cvR79+/XD37l1YW1vj4cOH6Ny5M3x9fbFhwwZcvXoVfn5+MDIywpw5cwAAU6dOxbZt27B+/Xo4OTkhJCQE3t7euHnzJqytrYt8PGlpafjmm2+wfv16GBgYYNSoUfj888+lvHfs2IHx48djyZIlaN++Pfbs2YNBgwahatWqaNu2Lfr164fevXvj1atXMDMzAwDs27cPqamp6NGjBwBg1qxZ2L59O0JDQ+Hq6orDhw+jf//+sLGxQevWraVcpk6dioULF8LFxQVGRkaYPHkyIiIi8NdffwEALC0tUatWLbRq1QoJCQmwt8/+OXjv3r149eoVevfurfIYMzIykJGRIU0nJycX+XUiKmkn45/na/kdYHxOOUgGhCw4DgBwdXVF3759pUULFy7MV5DmcHJygq+vrzS9dOlSpKWlqYytUqUK/Pz8pOmVK1ciKSlJZayNjQ1GjRolTa9evRpPnjxRGWuJgZiAtQB0sluB6/cAdPkxRUTqxxbgEpCQkICsrCx0794dzs7OcHd3x6hRo2BmZoZ58+Zh8uTJGD9+PGrVqoUPPvgAEyZMUFrf19cXffr0Qc2aNREUFITU1FScPHkSALBq1So4OjpixYoVqFOnDrp27YrAwEAsWrQICoUCqampCA0NxbfffotOnTrBzc0Nq1evhrGxMdauXVus45HJZFixYgWaNWuGJk2aYP369YiJiZFyWrhwIXx9fTFq1CjUqlULkyZNQvfu3bFw4UIAgLe3N0xNTbFjxw5pmz///DO6dOkCCwsLpKam4rvvvsO6devg7e0NFxcX+Pr6on///vjhhx+Ucpk7dy46dOiAGjVqwMHBAWZmZtDT04OdnR3s7OxgbGyM5s2b52uFDgsLQ69evaQCPK/g4GBYWlpKD0dHx2K9VkQlKTEl/e1B5YKCrcBEVKL41boEeHh4oF27dnB3d4e3tzc6duyInj17QiaT4dGjR2jXrt0b12/QoIH03NTUFObm5khMTAQAXLlyBc2aNYMgCFKMl5cXXr16hQcPHuDly5eQyWTw8vKSluvr68PT0xNXrlwp1vHo6emhadOm0nSdOnVgZWWFK1euSNsdNmyY0jpeXl5YunSptP9evXph06ZNGDBgAFJTU/H777/j559/BgDExcUhPT0dHTp0UNpGZmYmGjVqpDQvdx5vMnToUPz444+YOnUqEhMT8ccff+DAgQMFxs+YMQOTJk2SppOTk1kE03vH1two37yNrxvlmxc2yBMfVreGjo5yG4e/v3+B2879ngIA48ePL3Ts6NGjUdA9lfLG+vn5KcfKs4DvvYCk+xCUOnewFZiISg7fVUqArq4uIiMjERMTg/3792P58uWYOXPmGwuw3PT19ZWmBUGAQqEAkN1XNu8HSs6HiSAISs/zxuSdVxSq1s09723769evH1q3bo3ExERERkbCyMgInTp1AgDp2P744w84ODgobcfQ0FBp2tTUtFD5fvHFF5g+fTqOHz+O48ePw9nZGS1btiww3tDQMN++iN43ntWtYW9phMdJ6VKpmAVdabkAwM7SCM1dK0NXJ///WQMDg0Lvqyixed+zihR7fjuQdFtFZK5WYI4IQURqxi4QJUQQBHh5eSEwMBDnzp2DgYEBIiMj4ezsXOhCWBU3NzfExMQotaDExMTA3NwcDg4OqFmzJgwMDHD06FFpuUwmw+nTp1G3bt1i7TMrKwunT5+Wpq9du4aXL1+iTp06AIC6desq7S8np9z7a968ORwdHbF582Zs2rQJvXr1kj5g3dzcYGhoiHv37qFmzZpKj7e1whoYGEAul+ebX7FiRXTt2hVhYWEICwvDoEGDinXsRO8TXR0BAV3cAGQXu7nlTAd0cVNZ/L6XpJEfCspXhyNCEJUz+vr6mDFjBmbMmFGkL8/qxhbgEnDixAkcOHAAHTt2hK2tLU6cOIEnT56gbt26mDNnDkaMGAFbW1t06tQJKSkpOHbsGMaOHVuobY8aNQpLlizB2LFjMWbMGFy7dg0BAQGYNGkSdHR0YGpqipEjR2LKlCmwtrZGtWrVEBISgrS0NAwZMqRYx6Ovr4+xY8di2bJl0NfXx5gxY/B///d/8PT0BABMmTIFvXv3RuPGjdGuXTvs3r0b27dvly5MA7K/EPTt2xfff/89rl+/jkOHDknLzM3N4e/vj4kTJ0KhUKBFixZITk5GTEwMzMzMMHDgwAJzc3Z2Rnx8PGJjY1G1alWYm5tLLblDhw7FJ598Arlc/sZtEJUlPvXtEdq/cb5xgO3K4jjA944DL+++IeDfVuB7x4HqBf+CQ0RlhyAIRfqFqcSIpHZxcXGit7e3aGNjIxoaGoq1atUSly9fLi3//vvvxdq1a4v6+vqivb29OHbsWGkZAHHHjh1K27O0tBTDwsKk6aioKPGDDz4QDQwMRDs7O3HatGmiTCaTlr9+/VocO3asWKlSJdHQ0FD08vIST548KS0/dOiQCEB88eLFW48lLCxMtLS0FLdt2ya6uLiIBgYG4kcffSTeuXNHKW7VqlWii4uLqK+vL9aqVUvcsGFDvm1dvnxZBCA6OTmJCoVCaZlCoRCXLl0qvS42Njait7e3GB0d/cac09PTxR49eohWVlYiAKXXSaFQiE5OTmLnzp3fepx5JSUliQDEpKSkIq9LVBqy5Aox5uZTcee5B2LMzadillzx9pXeN7J0Uby0XRRjfy34cWl7dhwRUSEU9vNbEEXec5IKFh4ejgkTJpTJ2yanpaWhSpUqWLduHbp3716kdZOTk2FpaYmkpCRYWFiUUIZERETaJSsrC3v27AEAfPLJJ9DTU29nhMJ+frMPMJU7CoUCjx49wldffQVLS0v873//03RKREREhOzP6PPnz+P8+fPSRfCawD7AWq5Tp044cuSIymVffvklqlSpUsoZvbt79+6hevXqqFq1KsLDw9X+7ZKIiIjKNlYGWm7NmjV4/fq1ymXW1tawtrZWujtUWeDs7FzgmKRERERELIC1XN5xd4mIiIjKO/YBJiIiIiKtwgKYiIiIiLQKC2AiIiIi0irsA0xEREREpUJfXx/+/v7Sc01hAUxEREREpUIQBJiammo6DXaBICIiIiLtwhZgIiIiIioVWVlZ2LdvHwDA29tbYzerYgswEREREZUKhUKB06dP4/Tp0xq9FTILYCIiIiLSKiyAiYiIiEirsAAmIiIiIq3CApiIiIiItAoLYCIiIiLSKiyAiYiIiEircBxgIiIiIioV+vr6GD9+vPRcU1gAExEREVGpEAQBVlZWmk6DXSCIiIiISLuwBZiIiIiISoVcLseBAwcAAO3atYOurq5G8mALMBERERGVCrlcjuPHj+P48eOQy+Uay4MFMBERERFpFRbARERERKRVWAATERERkVZhAUxEREREWoUFMBERERFpFRbARERERKRVOA4wEREREZUKfX19jBw5UnquKSyAiYiIiKhUCIIAW1tbTadRvC4QEREROHr0qDS9cuVKNGzYEH379sWLFy/UlhwRERERkboVqwCeMmUKkpOTAQAXL17E5MmT0blzZ9y+fRuTJk1Sa4JEREREVD7I5XJERUUhKipKo3eCK1YXiPj4eLi5uQEAtm3bhk8++QRBQUE4e/YsOnfurNYEiYiIiKh8kMvliI6OBgA0b94curq6GsmjWC3ABgYGSEtLAwD89ddf6NixIwDA2tpaahkmIiIiInofFasFuEWLFpg0aRK8vLxw8uRJbN68GQBw/fp1VK1aVa0JEhERERGpU7FagFesWAE9PT1s3boVoaGhcHBwAAD8+eef8PHxUWuCqgiCgJ07d5boPqKioiAIAl6+fFli+wgPD4eVldU7b8fZ2RlLlix55+2Uhf2XxrknIvWQK0Qcv/UMv8c+xPFbzyBXiKoDRRF4eCb7XyKiUlCsFuBq1aphz549+eYvXrz4nRMqr5ydnTFhwgRMmDBB7ds+deoUTE1N1b7d93H/CQkJqFChAgDgzp07qF69Os6dO4eGDRuWyv6JqHAiLiUgcHccEpLSpXn2lkYI6OIGn/r2ysEXNgM7hgPdfgQ8PivlTIlIGxX7TnC3bt3CrFmz0KdPHyQmJgLIHh7t8uXLakuOCsfGxgYmJiblev+ZmZkAADs7OxgaGpbovojo3URcSsDIn84qFb8A8DgpHSN/OouISwn/zZRnAYeCsp9HBWVPExGVsGIVwNHR0XB3d8eJEyewfft2vHr1CgBw4cIFBAQEFHo7W7duhbu7O4yNjVGxYkW0b98eqampAIB169ahXr16MDQ0hL29PcaMGaO07tOnT9GtWzeYmJjA1dUVu3btypejp6entP706dORlfXfG2tGRgbGjRsHW1tbGBkZoUWLFjh16lRxXg4AQExMDFq1agVjY2M4Ojpi3Lhx0rG0adMGd+/excSJEyEIAgRBUFp33759qFu3LszMzODj44OEhP8+HHx9fdG1a1csXLgQ9vb2qFixIkaPHg2ZTCbF5O2C8PLlSwwbNgyVK1eGkZER6tevr7LFPq+cLhl79uxB7dq1YWJigp49eyI1NRXr16+Hs7MzKlSogLFjxyoNXZJ3/4IgYM2aNe90ftq0aYMxY8Zg0qRJqFSpEjp06CBtO6cLRPXq1QEAjRo1giAIaNOmDQ4fPgx9fX08fvxYaX+TJ09Gq1at3voaENG7kStEBO6OQ+7ODHqQQw9y6P7777xdF/E6PQOZmZnIOr8FeHk3O/DFHeDSNk2kTURaplgF8PTp0zFv3jxERkbCwMBAmt+2bVscP368UNtISEhAnz59MHjwYFy5cgVRUVHo3r07RFFEaGgoRo8ejWHDhuHixYvYtWsXatasqbR+YGAgevfujQsXLqBz587o168fnj9/DgB4+PAhOnfujA8++ADnz59HaGgo1q5di3nz5knrT506Fdu2bcP69etx9uxZ1KxZE97e3tI2iuLixYvw9vZG9+7dceHCBWzevBlHjx6Vivbt27ejatWqmDt3LhISEpQK3LS0NCxcuBAbN27E4cOHce/ePfj7+ytt/9ChQ7h16xYOHTqE9evXIzw8HOHh4SpzUSgU6NSpE2JiYvDTTz8hLi4O8+fPL/QwI2lpaVi2bBl+/fVXRERESOdl79692Lt3LzZu3Igff/wRW7dufeN23vX8AMD69euhp6eHY8eO4Ycffsi3j5MnTwLIHokkISEB27dvR6tWreDi4oKNGzdKcVlZWfjpp58waNCgAvPNyMhAcnKy0oOIiu5k/PN8Lb8DjM9Jj/7G59BBdhwhC+YjODgYW/48DCCnUUCHrcBE5Zyenh6GDh2KoUOHQk9PgzckFovB1NRUvH37tiiKomhmZibeunVLFEVRjI+PFw0NDQu1jTNnzogAxDt37uRbVqVKFXHmzJkFrgtAnDVrljT96tUrURAE8c8//xRFURS//PJLsXbt2qJCoZBiVq5cKZqZmYlyuVx89eqVqK+vL27atElanpmZKVapUkUMCQkRRVEUDx06JAIQX7x48dZjGTBggDhs2DCleUeOHBF1dHTE169fi6Ioik5OTuLixYuVYsLCwkQA4s2bN5XyrFy5sjQ9cOBA0cnJSczKypLm9erVS/zss8+k6dzb3rdvn6ijoyNeu3btrXnnpSqf4cOHiyYmJmJKSoo0z9vbWxw+fLjK/Yviu58fURTF1q1biw0bNsyXIwBxx44doihm/70BEM+dO6cUs2DBArFu3brS9M6dO0UzMzPx1atXBR57QECACCDfIykpqcB1iCi/neceiE7T9ig95syZU+BjU8AXohhgofyI/VXTh0FEZVRSUlKhPr+L1QJsZWWl1IqZ49y5c9KIEG/j4eGBdu3awd3dHb169cLq1avx4sULJCYm4tGjR2jXrt0b12/QoIH03NTUFObm5lJf5CtXrqBZs2ZKXQ28vLzw6tUrPHjwALdu3YJMJoOXl5e0XF9fH56enrhy5Uqh8s/tzJkzCA8Ph5mZmfTw9vaGQqFAfHz8G9c1MTFBjRo1pGl7e3vpOHLUq1dPqQVXVUyO2NhYVK1aFbVq1SrycajKp3LlynB2doaZmZnSvIL2n+Ndzk+Opk2bFusYfH19cfPmTfz9998AsrvT9O7d+40X6s2YMQNJSUnS4/79+8XaN5G2szU3yjdv4+tG+R4f9R6CGRY70Rt5u2exFZiISl6xCuC+ffti2rRpePz4MQRBgEKhwLFjx+Dv748vvviiUNvQ1dVFZGQk/vzzT7i5uWH58uWoXbs2/vnnn0Ktr6+vrzSdkwcAiKKYr5+t+O/wOoIgKD3PG5N3XmEoFAoMHz4csbGx0uP8+fO4ceOGUjFZ2OMQ8wwF9KZjzcvY2LjI+b9tX0XZ/5u2U9jzk6O4I0vY2tqiS5cuCAsLQ2JiIvbu3YvBgwe/cR1DQ0NYWFgoPYio6DyrW8Pe0gi5/4dnQVd6yKELG0tTeGUcgUHSbeghb6GrYF9gonJMLpfj2LFjOHbsmEZvhVysAvibb75BtWrV4ODggFevXsHNzQ2tWrVC8+bNMWvWrEJvRxAEeHl5ITAwEOfOnYOBgQEiIyPh7OyMAwcOFCc1AICbmxtiYmKUCsmYmBiYm5vDwcEBNWvWhIGBAY4ePSotl8lkOH36NOrWrVvk/TVu3BiXL19GzZo18z1y+kgbGBiUyolu0KABHjx4gOvXr5f4vorrbeensHJeW1Wv69ChQ/Hrr7/ihx9+QI0aNZRa+4mo5OjqCAjo4gYAyNuckDM95+Na0IkKVhGRg63AROWVXC7HX3/9hb/++qvsFcD6+vrYtGkTbty4gS1btuCnn37C1atXsXHjxkJfbHXixAkEBQXh9OnTuHfvHrZv344nT56gbt26mDNnDhYtWoRly5bhxo0bOHv2LJYvX17o/EaNGoX79+9j7NixuHr1Kn7//XcEBARg0qRJ0NHRgampKUaOHIkpU6YgIiICcXFx8PPzQ1paGoYMGVLk12PatGk4fvw4Ro8ejdjYWNy4cQO7du3C2LFjpRhnZ2ccPnwYDx8+xNOnT4u8j8Jq3bo1WrVqhR49eiAyMhLx8fH4888/ERERUWL7LKq3nZ/CsrW1hbGxMSIiIvDPP/8gKSlJWubt7Q1LS0vMmzfvjRe/EZH6+dS3R2j/xrCzVO4OYWdphND+jeFtfuffkR8KuvHFv63A9wp3UTURUVEV6/K7uXPnwt/fHy4uLnBxcZHmv379Gt9++y1mz5791m1YWFjg8OHDWLJkCZKTk+Hk5IRFixahU6dOAID09HQsXrwY/v7+qFSpEnr27Fno/BwcHLB3715MmTIFHh4esLa2xpAhQ5Rap+fPnw+FQoEBAwYgJSUFTZs2xb59+6SbLBRFgwYNEB0djZkzZ6Jly5YQRRE1atTAZ5/9N6D73LlzMXz4cNSoUQMZGRn5ujmo07Zt2+Dv748+ffogNTUVNWvWxPz580tsf0VVmPNTGHp6eli2bBnmzp2L2bNno2XLloiKigIA6OjowNfXF0FBQYXulkNE6uNT3x4d3OxwMv45ElPSYWtuBM/q1tDVEYAsa6BXOJCVWfAG9AwAR89Sy5eItIsgFqMS09XVRUJCAmxtbZXmP3v2DLa2thpt0ibK4efnh3/++SffGMSFkZycDEtLSyQlJbE/MBERkZpkZmYiODgYQPYF6LmH01WHwn5+F6sFuKCLxc6fPw9ra+vibJJIbZKSknDq1Cls2rQJv//+u6bTISIiovdMkfoAV6hQAdbW1hAEAbVq1YK1tbX0sLS0RIcOHdC7d++SylVjOnXqpDTEWe5HUFCQptMrtPJyHG/z6aef4n//+x+GDx8u3UGOiIiIKEeRWoCXLFkCURQxePBgBAYGwtLSUlpmYGAAZ2dnNGvWTO1JatqaNWvw+vVrlcvKUot3eTmOt8npB0xERESkSrH6AEdHR6N58+b5xnolKi/YB5iIiEj9FAoF7t27BwCoVq1akUZ/KowS7QPcunVr6fnr168hk8mUlrNgICIiIqK8dHR04OzsrOk0ijcOcFpaGsaMGQNbW1uYmZmhQoUKSg8iIiIiovdVsQrgKVOm4ODBg1i1ahUMDQ2xZs0aBAYGokqVKtiwYYO6cyQiIiKickAul+PkyZM4efKkRofNLVYXiN27d2PDhg1o06YNBg8ejJYtW6JmzZpwcnLCpk2b0K9fP3XnSURERERlnFwux59//gkAaNiwYaHvIKxuxWoBfv78OapXrw4gu7/v8+fPAQAtWrTA4cOH1ZcdEREREZGaFasAdnFxwZ07dwAAbm5u2LJlC4DslmErKyt15UZEREREpHbFKoAHDRqE8+fPA8i+jV1OX+CJEydiypQpak2QiIiIiEiditUHeOLEidLztm3b4urVqzh9+jRq1KgBDw8PtSVHRERERKRuxSqA86pWrRqqVaumjk0REREREZWoQhfAy5Ytw7Bhw2BkZIRly5a9MXbcuHHvnBgRERERUUko9K2Qq1evjtOnT6NixYrSCBAqNygIuH37ttoSJNIE3gqZiIhI/RQKBW7evAkAqFmz5vt/K+T4+HiVz4mIiIiICkNHRwe1atXSdBpFHwVCJpPBxcUFcXFxJZEPEREREVGJKvJFcPr6+sjIyIAgCCWRDxERERGVU3K5HBcvXgQAuLu7l607wY0dOxYLFixAVlaWuvMhIiIionJKLpfj999/x++//w65XK6xPIo1DNqJEydw4MAB7N+/H+7u7jA1NVVavn37drUkR0RERESkbsUqgK2srNCjRw9150JEREREVOKKVQCHhYWpOw8iIiIiolKh3sHXiIiIiIjec8W+FfLWrVuxZcsW3Lt3D5mZmUrLzp49+86JERERERGVhGK1AC9btgyDBg2Cra0tzp07B09PT1SsWBG3b99Gp06d1J0jEREREZHaFKsFeNWqVfjxxx/Rp08frF+/HlOnToWLiwtmz56N58+fqztHIiIiIioH9PT00LNnT+m5phSrBfjevXto3rw5AMDY2BgpKSkAgAEDBuCXX35RX3ZEREREVG7o6OigXr16qFevHnR0NHcpWrH2bGdnh2fPngEAnJyc8PfffwMA4uPjIYqi+rIjIiIiIlKzYhXAH330EXbv3g0AGDJkCCZOnIgOHTrgs88+Q7du3dSaIBERERGVDwqFApcvX8bly5ehUCg0locgFqPJVqFQQKFQSH03tmzZgqNHj6JmzZoYMWIEDAwM1J4oUWlKTk6GpaUlkpKSYGFhoel0iIiIyoXMzEwEBwcDAGbMmKH2mrGwn9/F6n2so6Oj1G+jd+/e6N27d3E2RURERERUqorVBaJ69er46quvcPXqVXXnQ0RERERUoopVAI8dOxYRERFwc3NDkyZNsGTJEiQkJKg7NyIiIiIitStWATxp0iScOnUKV69exSeffILQ0FBUq1YNHTt2xIYNG9SdIxERERGR2rzTAGy1atVCYGAgrl27hiNHjuDJkycYNGiQunIjIiIiIlK7d74Fx8mTJ/Hzzz9j8+bNSEpKku7uQURERET0PipWC/D169cREBAAV1dXeHl5IS4uDvPnz8c///yDzZs3qzvHMkMQBOzcubNE9xEVFQVBEPDy5csS3Y+zszOWLFlSovsAAF9fX3Tt2rVQsXfu3IEgCIiNjS3RnKh8kytEHL/1DL/HPsTxW88gV2j5zXtEEXh4JvtfIqISpquri08//RSffvopdHV1NZZHsVqA69Spg6ZNm2L06NH4/PPPYWdnp+68SEssXbq00HcPdHR0REJCAipVqgQg+8tA27Zt8eLFC1hZWZVgllReRFxKQODuOCQkpUvz7C2NENDFDT717TWYmQZd2AzsGA50+xHw+EzT2RBROaerq4uGDRtqOo3iFcBXr15FrVq11J0LvQcyMzNL9UYmlpaWhY7V1dXlly0qtohLCRj501nk/br1OCkdI386i9D+jbWvCJZnAYeCsp9HBQH1ewC679wzjojovVesLhA5xe+ZM2fw008/YdOmTTh79qxaE9OkrVu3wt3dHcbGxqhYsSLat2+P1NRUAMC6detQr149GBoawt7eHmPGjFFa9+nTp+jWrRtMTEzg6uqKXbt2KS2Pjo6Gp6entP706dORlZUlLc/IyMC4ceNga2sLIyMjtGjRAqdOnSrWccyZMyfft6wlS5bA2dlZms7pghAcHIwqVaoofbFJSUlB3759YWZmhipVqmD58uVK2/ruu+/g7u4OU1NTODo6YtSoUXj16pW0PDw8HFZWVti3bx/q1q0LMzMz+Pj4KA2Zl7cLhEKhwIIFC1CzZk0YGhqiWrVq+OabbwAod4G4c+cO2rZtCwCoUKECBEGAr68vNmzYgIoVKyIjI0Mp1x49euCLL74o1utIZZ9cISJwd5xS8asHOfQgh+6//87bdRGv0zOQmZmp9H8SyP5iWNBDJpMVO1Ymk5V6bGZm5n+Bl7ZC9vJh9vMXd4BL24r1+hIRFZZCocD169dx/fp1jd4KuVhf9RMTE/H5558jKioKVlZWEEURSUlJaNu2LX799VfY2NioO89Sk5CQgD59+iAkJATdunVDSkoKjhw5AlEUERoaikmTJmH+/Pno1KkTkpKScOzYMaX1AwMDERISgm+//RbLly9Hv379cPfuXVhbW+Phw4fo3LmzVKhdvXoVfn5+MDIywpw5cwAAU6dOxbZt27B+/Xo4OTkhJCQE3t7euHnzJqytrUvkmA8cOAALCwtERkYqdUf49ttv8eWXX2LOnDnYt28fJk6ciDp16qBDhw4Asu8IuGzZMjg7OyM+Ph6jRo3C1KlTsWrVKmkbaWlpWLhwITZu3AgdHR30798f/v7+2LRpk8pcZsyYgdWrV2Px4sVo0aIFEhISVN5wxdHREdu2bUOPHj1w7do1WFhYwNjYGAYGBhg3bhx27dqFXr16Acj+UrJnzx5EREQU+BpkZGQoFc3JyclFexHpvXYy/rlStwcAGGB8TjlIBoQsOA4AcHV1Rd++faVFCxcuzFdk5nBycoKvr680vXTpUqSlpamMrVKlCvz8/KTplStXIikpSWWsjY0NRo0aJU2vXr0aT548URlraWmJCRMmSNPh4eF49OiRylgTExNMmTJFav3dhG7wxW8AdNgKTEQlLisrC7/88guAkrkVcmEV611u7NixSE5OxuXLl1G3bl0AQFxcHAYOHIhx48ZJB1YWJSQkICsrC927d4eTkxMAwN3dHQAwb948TJ48GePHj5fiP/jgA6X1fX190adPHwBAUFAQli9fjpMnT8LHxwerVq2Co6MjVqxYAUEQUKdOHTx69AjTpk3D7Nmz8fr1a4SGhiI8PBydOnUCkP2hFxkZibVr12Z/aJUAU1NTrFmzJt8foZeXF6ZPnw4gu9X/2LFjWLx4sVQA5/7ArV69Or7++muMHDlSqQCWyWT4/vvvUaNGDQDAmDFjMHfuXJV5pKSkYOnSpVixYgUGDhwIAKhRowZatGiRL1ZXV1f6QmBra6vUB7hv374ICwuTCuBNmzahatWqaNOmTYGvQXBwMAIDAwtcTmVbYkr624O0zaWtwMu7ADz/naH4rxWYfYGJqJwrVgEcERGBv/76Syp+AcDNzQ0rV65Ex44d1ZacJnh4eKBdu3Zwd3eHt7c3OnbsiJ49e0Imk+HRo0do167dG9dv0KCB9NzU1BTm5uZITEwEAFy5cgXNmjWDIAhSjJeXF169eoUHDx7g5cuXkMlk8PLykpbr6+vD09MTV65cUfOR/sfd3V3lN7BmzZrlm849MsShQ4cQFBSEuLg4JCcnIysrC+np6UhNTYWpqSmA7NamnOIXAOzt7aXXI68rV64gIyPjra/x2/j5+eGDDz7Aw4cP4eDggLCwMPj6+iq97nnNmDEDkyZNkqaTk5Ph6Oj4TnnQ+8PW3CjfvI2vG+WbFzbIEx9Wt4aOjnLvMH9//wK3nffvKvcX5LfFjh49usCLQPPG+vn5FTrW19f3zReXSn1/BfTDjlwL2ApMRNqhWH2AFQoF9PX1883X19fXaH8OddDV1UVkZCT+/PNPuLm5Yfny5ahduzb++eefQq2f93URBEF6TURRzPdBlfMhJQiC0vO8MW8q3gqio6OT70NQ1c+4OcVqYeTkcffuXXTu3Bn169fHtm3bcObMGaxcuTLfPlS9HgV9MBsbGxc6jzdp1KgRPDw8sGHDBpw9exYXL15U+olaFUNDQ1hYWCg9qPzwrG4Ne0sj5P5flAVd6SGHLmwsTdHctTIMDAygp6dc/BkYGBT4yPs3XpRYfX39Uo81MDDI1forQh+5+zsr2BeYiLRCsQrgjz76COPHj1fqY/bw4UNMnDjxnVvv3geCIMDLywuBgYE4d+4cDAwMEBkZCWdnZxw4cKDY23Vzc0NMTIxSARgTEwNzc3M4ODigZs2aMDAwwNGjR6XlMpkMp0+fVmptLywbGxs8fvxYaX9FGUP377//zjddp04dAMDp06eRlZWFRYsW4f/+7/9Qq1atAvscFparqyuMjY0L/RrntFrL5fJ8y4YOHYqwsDCsW7cO7du3Z2uultPVERDQxQ0AkPerZM50QBc36OoU/YtmmZOr9Ve1f1uB5VkFLCciKvuKVQCvWLECKSkpcHZ2Ro0aNVCzZk1Ur14dKSkp+UYKKGtOnDiBoKAgnD59Gvfu3cP27dvx5MkT1K1bF3PmzMGiRYuwbNky3LhxA2fPni3S8Y4aNQr379/H2LFjcfXqVfz+++8ICAjApEmToKOjA1NTU4wcORJTpkxBREQE4uLi4Ofnh7S0NAwZMqTIx9KmTRs8efIEISEhuHXrFlauXIk///yz0OsfO3YMISEhuH79OlauXInffvtN+nm3Ro0ayMrKwvLly3H79m1s3LgR33//fZFzzM3IyAjTpk3D1KlTsWHDBty6dQt///031q5dqzLeyckJgiBgz549ePLkidIIFP369cPDhw+xevVqDB48+J3yovLBp749Qvs3hp2lcncIO0sj7RoC7d5xqfVXtX9bge8dL8WkiIhKV7E6eTk6OuLs2bOIjIzE1atXIYoi3Nzc0L59e3XnV+osLCxw+PBhLFmyBMnJyXBycsKiRYuki9LS09OxePFi+Pv7o1KlSkW69bODgwP27t2LKVOmwMPDA9bW1hgyZAhmzZolxcyfPx8KhQIDBgxASkoKmjZtin379qFChQpFPpa6deti1apVCAoKwtdff40ePXrA398fP/74Y6HWnzx5Ms6cOYPAwECYm5tj0aJF8Pb2BgA0bNgQ3333HRYsWIAZM2agVatWCA4Ofuehxr766ivo6elh9uzZePToEezt7TFixAiVsQ4ODggMDMT06dMxaNAgfPHFFwgPDweQfR579OiBP/74o9B3mqPyz6e+PTq42eFk/HMkpqTD1twIntWttaPlN4ejJ9ArHMjKLDhGzyA7joionBLEwt6GC8DBgwcxZswY/P333/n6SCYlJaF58+b4/vvv0bJlS7UnSlRUHTp0QN26dbFs2bIir5ucnAxLS0skJSWxPzAREZGayOVynDlzBgDQpEkTtd8OubCf30VqAV6yZAn8/PxUbtDS0hLDhw/Hd999xwKYNOr58+fYv38/Dh48iBUrVmg6HSIiIvqXrq4uPD01/wtTkfoAnz9/Hj4+PgUu79ixo1TVU8nr1KkTzMzMVD6CgoI0nZ7GNG7cGMOHD8eCBQtQu3ZtTadDRERE75kitQD/888/Koc/kzamp1fgnYpI/dasWYPXr1+rXFZSd40rC+7cuaPpFIiIiEgFhUKBe/fuAQCqVauWb9z10lKkAtjBwQEXL15EzZo1VS6/cOEC7O215Erq94CDg4OmUyAiIiIqtKysLKxfvx6AZm+FXKSyu3Pnzpg9ezbS0/PfVvT169cICAjAJ598orbkiIiIiIjUrUgtwLNmzcL27dtRq1YtjBkzBrVr14YgCLhy5QpWrlwJuVyOmTNnllSuRERERETvrEgFcOXKlRETE4ORI0dixowZSrfu9fb2xqpVq1C5cuUSSZSIiIiISB2KfCMMJycn7N27Fy9evMDNmzchiiJcXV2LdaMGIiIiIqLSVqw7wQFAhQoV8MEHH6gzFyIiIiKiEqeZsSeIiIiIiDSk2C3ARERERERFoauri/bt20vPNYUFMBERERGVCl1dXXh5eWk6DXaBICIiIiLtwhZgIiIiIioVCoUCCQkJAAB7e3uN3QqZLcBEREREVCqysrKwZs0arFmzBllZWRrLgwUwEREREWkVFsBEREREpFVYABMRERGRVmEBTERERERahQUwEREREWkVFsBEREREpFU4DjARERERlQpdXV20bt1aeq4pLICJiIiIqFTo6uqiTZs2mk6DXSCIiIiISLuwBZiIiIiISoUoinjy5AkAwMbGBoIgaCQPtgATERERUamQyWQIDQ1FaGgoZDKZxvJgAUxEREREWoUFMBERERFpFRbARERERKRVWAATERERkVZhAUxEREREWoUFMBERERFpFY4DTERERESlQldXF82aNZOeawoLYCIiIiIqFbq6uujYsaOm02AXCCIiIiLSLmwBJiIiIqJSIYoikpKSAACWlpa8FbImCIKAnTt3lug+oqKiIAgCXr58WaL7AYA7d+5AEATExsYWGBMeHg4rK6sSz4WIygBRBB6eyf6X1EquEHH81jP8HvsQx289g1zB15gIyL4V8tKlS7F06VKN3gqZLcBa5rPPPkPnzp01nUapmTNnDnbu3PnGLwVEWuvCZmDHcKDbj4DHZ5rOptyIuJSAwN1xSEhKl+bZWxohoIsbfOrbazAzIsqh1S3A2sjY2Bi2traaToOINE2eBRwKyn4eFZQ9Te8s4lICRv50Vqn4BYDHSekY+dNZRFxK0FBmRJRbuSiAt27dCnd3dxgbG6NixYpo3749UlNTAQDr1q1DvXr1YGhoCHt7e4wZM0Zp3adPn6Jbt24wMTGBq6srdu3apbQ8Ojoanp6e0vrTp09HVtZ/HxQZGRkYN24cbG1tYWRkhBYtWuDUqVPFOo5nz56hT58+qFq1KkxMTODu7o5ffvlFKUahUGDBggWoWbMmDA0NUa1aNXzzzTdKMbdv30bbtm1hYmICDw8PHD9+XFqmqgvE7t270aRJExgZGcHFxQWBgYHSMfbp0weff/65UrxMJkOlSpUQFhYGILs/T0hICFxcXGBsbAwPDw9s3bpVis/pBrJv3z40atQIxsbG+Oijj5CYmIg///wTdevWhYWFBfr06YO0tDRpvcJu98CBA2jatClMTEzQvHlzXLt2TTrWwMBAnD9/HoIgQBAEhIeHF/GsEJVTl7YCL+8iE3rIfPEAmbG/ITMzU+mR+70OQL7luR95f8osSqxMJiv12MzMzGLHZmVlqYx5nZ6BebsuQlVnh5x5gbvj2B2C6D1Q5rtAJCQkoE+fPggJCUG3bt2QkpKCI0eOQBRFhIaGYtKkSZg/fz46deqEpKQkHDt2TGn9wMBAhISE4Ntvv8Xy5cvRr18/3L17F9bW1nj48CE6d+4MX19fbNiwAVevXoWfnx+MjIwwZ84cAMDUqVOxbds2rF+/Hk5OTggJCYG3tzdu3rwJa2vrIh1Leno6mjRpgmnTpsHCwgJ//PEHBgwYABcXF3z44YcAgBkzZmD16tVYvHgxWrRogYSEBFy9elVpOzNnzsTChQvh6uqKmTNnok+fPrh58yb09PKf7n379qF///5YtmwZWrZsiVu3bmHYsGEAgICAAPTr1w+9e/fGq1evYGZmJq2TmpqKHj16AABmzZqF7du3IzQ0FK6urjh8+DD69+8PGxsbtG7dWtrXnDlzsGLFCpiYmKB3797o3bs3DA0N8fPPP+PVq1fo1q0bli9fjmnTphVpuzNnzsSiRYtgY2ODESNGYPDgwTh27Bg+++wzXLp0CREREfjrr78AZHe4VyUjIwMZGRnSdHJycpHOHVGZIrX+CggWxmXP23Md2BOsFObq6oq+fftK0wsXLiywz56TkxN8fX2l6aVLlyp9oc2tSpUq8PPzk6ZXrlwpXRSTl42NDUaNGiVNr169Gk+ePFEZa2lpiQkTJkjT4eHhePTokcpYExMTTJkyRZretGkT7t69qzJWX18fX375pTS9ZcsW3LhxQ2VsBwBhaKpymQggISkdJ+Ofo1mNiipjiKh0lIsCOCsrC927d4eTkxMAwN3dHQAwb948TJ48GePHj5fiP/jgA6X1fX190adPHwBAUFAQli9fjpMnT8LHxwerVq2Co6MjVqxYAUEQUKdOHTx69AjTpk3D7Nmz8fr1a4SGhiI8PBydOnUCkP3mHBkZibVr1yq9uRaGg4MD/P39pemxY8ciIiICv/32Gz788EOkpKRg6dKlWLFiBQYOHAgAqFGjBlq0aKG0HX9/f3z88ccAsgv8evXq4ebNm6hTp06+fX7zzTeYPn26tD0XFxd8/fXXmDp1KgICAuDt7Q1TU1Ps2LEDAwYMAAD8/PPP6NKlCywsLJCamorvvvsOBw8elAa2dnFxwdGjR/HDDz8oFarz5s2Dl5cXAGDIkCGYMWMGbt26BRcXFwBAz549cejQIUybNq1I2/3mm2+k6enTp+Pjjz9Geno6jI2NYWZmBj09PdjZ2b3xtQ8ODkZgYOAbY4jKjX9bf0kzElPS3x5ERCWqzBfAHh4eaNeuHdzd3eHt7Y2OHTuiZ8+ekMlkePToEdq1a/fG9Rs0aCA9NzU1hbm5ORITEwEAV65cQbNmzZSG6PDy8sKrV6/w4MEDvHz5EjKZTCrqgOyWAk9PT1y5cqXIxyKXyzF//nxs3rwZDx8+lFolTU1NpXwyMjKKdEz29tkXXCQmJqosgM+cOYNTp04pdaOQy+VIT09HWloaTExM0KtXL2zatAkDBgxAamoqfv/9d/z8888AgLi4OKSnp6NDhw5K283MzESjRo0KzKty5cowMTGRit+ceSdPnnyn7eY+3mrVqhX0EuUzY8YMTJo0SZpOTk6Go6NjodcnKjNytf4CImaIy/5doANYOQLDjwK62R8NOjrKveRyf0HPK+9QRrkbHt4WO3r0aIgFjESRN9bPz6/Qsb6+vgXG5tWvX79Cx/bu3RsKhSLf/BPxzzEo7ORb17c1NyrUfoio5JT5AlhXVxeRkZGIiYnB/v37sXz5csycORMHDhwo1Pr6+vpK04IgSG9soijme0PNeYMUBEHped6Y4oxrt2jRIixevBhLliyBu7s7TE1NMWHCBKn/mbGxcZGPKScPVW/WOfMDAwPRvXv3fMuMjLLfpPv164fWrVsjMTERkZGRMDIyklq8c7b7xx9/wMHBQWl9Q0PDN+b1ptf+Xbb7puMtiKGhYb7tEpVLeVp/DZCrn+/LW8D13QWOCGFgYFDo3RQlNu97wfseq6o7GQA0d60MG0tTPE5KV9kPWABgZ2kEz+pF6x5HVJ7o6OigadOm0nNNKfMFMJBd9Hh5ecHLywuzZ8+Gk5MTIiMj4ezsjAMHDqBt27bF2q6bmxu2bdumVNDGxMTA3NwcDg4OsLa2hoGBAY4ePSr1k5PJZDh9+rRSP7TCOnLkCD799FP0798fQHYRd+PGDdStWxdAdn88Y2NjHDhwAEOHDi3WMeXVuHFjXLt2DTVr1iwwpnnz5nB0dMTmzZvx559/olevXtKHm5ubGwwNDXHv3j2lbgnvSl3bNTAwgFwuV1teRGVantbf/HSyR4So30NqBabC09URENDFDSN/OpvvFc5pEgno4gZdHc0M/E/0PtDT05O6aWo0D00n8K5OnDiBAwcOoGPHjrC1tcWJEyfw5MkT1K1bF3PmzMGIESNga2uLTp06ISUlBceOHcPYsWMLte1Ro0ZhyZIlGDt2LMaMGYNr164hICAAkyZNgo6ODkxNTTFy5EhMmTIF1tbWqFatGkJCQpCWloYhQ4YU+Vhq1qyJbdu2ISYmBhUqVMB3332Hx48fSwWwkZERpk2bhqlTp8LAwABeXl548uQJLl++XKz9AcDs2bPxySefwNHREb169YKOjg4uXLiAixcvYt68eQCyv2D07dsX33//Pa5fv45Dhw5J65ubm8Pf3x8TJ06EQqFAixYtkJycjJiYGJiZmUl9i4tKXdt1dnZGfHw8YmNjUbVqVZibm7Oll7TXveNv6furAF7cyY6r3rK0sipXfOrbI7R/43zjANtxHGCi90qZL4AtLCxw+PBhLFmyBMnJyXBycsKiRYukn+jT09OxePFi+Pv7o1KlSujZs2eht+3g4IC9e/diypQp8PDwgLW1NYYMGYJZs2ZJMfPnz4dCocCAAQOQkpKCpk2bYt++fahQoUKRj+Wrr75CfHw8vL29YWJigmHDhqFr165KV0d/9dVX0NPTw+zZs/Ho0SPY29tjxIgRRd5XDm9vb+zZswdz585FSEgI9PX1UadOnXwtzP369UNQUBCcnJyU+jwDwNdffw1bW1sEBwfj9u3bsLKyQuPGjZWumi4OdWy3R48e2L59O9q2bYuXL18iLCxM6Up1Iq3i6An0CgeyMguO0TPIjqNi86lvjw5udjgZ/xyJKemwNc/u9sCWX6LsbqI5I8SYmJho7FbIgljYXv9EWiQ5ORmWlpZISkqChYWFptMhIiIqFzIzMxEcnD3k4owZM4p0vUBhFPbzu1zcCIOIiIiIqLBYAJeiTp06wczMTOUjKChI0+kRERERaYUy3we4LFmzZg1ev36tcllR7xpHRERERMXDArgU5R3PloiIiIhKH7tAEBEREZFWYQFMRERERFqFXSCIiIiIqFTo6OjAw8NDeq4pLICJiIiIqFTo6emha9eumk6DXSCIiIiISLuwBZiIiIiISoUoipDJZAAAfX19jd0KmS3ARERERFQqZDIZgoODERwcLBXCmsACmIiIiIi0CgtgIiIiItIqLICJiIiISKuwACYiIiIircICmIiIiIi0CgtgIiIiItIqHAeYiIiIiEqFjo4O3NzcpOeawgKYiIiIiEqFnp4eevXqpek02AWCiIiIiLQLC2AiIiIi0irsAkFEREREpSIzMxPBwcEAgBkzZsDAwEAjebAFmIiIiIi0CgtgIiIiItIqLICJiIiISKuwACYiIiIircICmIiIiIi0CgtgIiIiItIqHAaNiIiIiEqFjo4OXF1dpeeawgKYiIiIiEqFnp4e+vbtq+k02AWCiIiIiLQLC2AiIiIi0irsAkFEREREpSIzMxMLFy4EAPj7+2vsVsgsgImIiIio1MhkMk2nwC4QRERERKRdWAATERERkVZhAUxlhq+vL7p27arpNIpNrhBx/NYz/B77EMdvPYNcIWo6JSopogg8PJP9LxERvXfYB5ioFERcSkDg7jgkJKVL8+wtjRDQxQ0+9e01mBmViAubgR3DgW4/Ah6faTobIiLKgy3ApDVEUURWVlap7zfiUgJG/nRWqfgFgMdJ6Rj501lEXEoo9ZyoBMmzgENB2c+jgrKniYjovcICmIosJSUF/fr1g6mpKezt7bF48WK0adMGEyZMAJA9xMnUqVPh4OAAU1NTfPjhh4iKipLWDw8Ph5WVFfbt24e6devCzMwMPj4+SEj4rxCUy+WYNGkSrKysULFiRUydOhVinp+TRVFESEgIXFxcYGxsDA8PD2zdulVaHhUVBUEQsG/fPjRt2hSGhoY4cuRIib42eckVIgJ3xyF35nqQQw9y6P7777xdF/E6PQOZmZn5CvTMzMwCH3mvoi1KrEwmK/XYzMzMYsdmZWWpLTb331GJxF7aCvnL+9nPX9wBLm0DERFlEwQBTk5OcHJygiAIGsuDXSCoyCZNmoRjx45h165dqFy5MmbPno2zZ8+iYcOGAIBBgwbhzp07+PXXX1GlShXs2LEDPj4+uHjxonT/77S0NCxcuBAbN26Ejo4O+vfvD39/f2zatAkAsGjRIqxbtw5r166Fm5sbFi1ahB07duCjjz6S8pg1axa2b9+O0NBQuLq64vDhw+jfvz9sbGzQunVrKW7q1KlYuHAhXFxcYGVlpfKYMjIykJGRIU0nJyer5bU6Gf88X8vvAONzykEyIGTBcQCAq6ur0i0iFy5cWOBwMU5OTvD19ZWmly5dirS0NJWxVapUgZ+fnzS9cuVKJCUlqYy1sbHBqFGjpOnVq1fjyZMnKmMtLS2lLz5A9pebR48eqYw1MTHBlClTpOlNmzbh7t27KmP19fXx5ZdfStNbtmzBjRs3VMYCQEBAgPR8x44diIuLKzB2xowZ0riTe/bswfnz5wuM9ff3h6mpKQBg3759OH36dIGx48ePh5W5GXAoCAfQAh1xGIBOditw/R6ALt9uiYj09fWVPrs0he/IVCQpKSlYv349fv75Z7Rr1w4AEBYWhipVqgAAbt26hV9++QUPHjyQ5vn7+yMiIgJhYWEICsr+aVgmk+H7779HjRo1AABjxozB3Llzpf0sWbIEM2bMQI8ePQAA33//Pfbt2yctT01NxXfffYeDBw+iWbNmAAAXFxccPXoUP/zwg1IBPHfuXHTo0OGNxxUcHIzAwMB3em1USUxJf3sQlR+XtgIv7wJw+neG4r9WYPYFJiJ6bwhi3t+Vid7g/PnzaNiwIe7evYtq1apJ8xs3boxWrVrBy8sLvXv3llrNcmRkZKB79+7YvHkzwsPDMXr0aKSmpkrLd+zYgR49ekChUCApKQlWVlaIjo5Gq1atpJhu3bpBFEXs3LkTp06dgqenZ779ZGZmolGjRjhx4gSioqLQtm1bPHjwAA4ODm88LlUtwI6OjkhKSoKFhUWxXisAOH7rGfqs/ltpnh7k+eLCBnniw+rW0NHRgZ7ef99L8/68n5sgCNDX1y9WrEwmy9elpKRjASjd8acosVlZWVAoFGqJ1dfXl352U2usjgBhRRPg5T3IIUAXObE6QIVqwJgzbAUmIiphycnJsLS0fOvnN9+NqUhyCpa8/XZy5isUCujq6uLMmTPQ1dVVijEzM5Oe5y6acrZXlO9iOYXIH3/8ka+4NTQ0VJrOWySrYmhomG89dfCsbg17SyM8TkqX+gFn4b/XRQBgZ2mE5q6VoauTvy9UUW4RWZTYvK//+x6b+0vBext7/td/W38BXaVe32wFJiLKkZmZiaVLlwLI7jqmqVsh8yI4KpIaNWpAX18fJ0+elOYlJydL/TMbNWoEuVyOxMRE1KxZU+lhZ2dXqH1YWlrC3t4ef//9X8tpVlYWzpw5I027ubnB0NAQ9+7dy7cfR0dHNR3tu9PVERDQxQ1AdrGbW850QBc3lcUvlSHSyA8FnUcdjghBRPSvtLS0Aq9ZKS1sAaYiMTc3x8CBAzFlyhRYW1vD1tYWAQEB0NHRgSAIqFWrFvr164cvvvgCixYtQqNGjfD06VMcPHgQ7u7u6Ny5c6H2M378eMyfPx+urq6oW7cuvvvuO7x8+VIpD39/f0ycOBEKhQItWrRAcnIyYmJiYGZmhoEDB5bQK1B0PvXtEdq/cb5xgO04DnD5ce+41Pqr2r+twPeOA9VbllZWRERUABbAVGTfffcdRowYgU8++QQWFhaYOnUq7t+/DyMjIwDZF8XNmzcPkydPxsOHD1GxYkU0a9as0MUvAEyePBkJCQnw9fWFjo4OBg8ejG7duimNXPD111/D1tYWwcHBuH37NqysrNC4cWOl0QPeFz717dHBzQ4n458jMSUdtuZG8KxuzZbf8sLRE+gVDmQV3A8begbZcUREpHG8CI7eWWpqKhwcHLBo0SIMGTJE0+moRWE70RMREVHhZWZmIjg4GIDysJTqwovgqMScO3cOV69ehaenJ5KSkqThyz799FMNZ0ZERET0diyAqVgWLlyIa9euwcDAAE2aNMGRI0dQqVIlTadFRERE9FYsgKnIGjVqpDQiAxEREVFhCIIg3SiLt0ImIiIionJPX18ffn5+mk6DBTARERGVHXK5HDKZTNNpkIbo6+vnu9FWcbAAJiIioveeKIp4/Pix0pjwpJ2srKxgZ2f3Tl0oWAATERHRey+n+LW1tYWJiYlG+49S8SkUCjx//hwAYG1tDR2dwt+UWBRFpKWlITExEQBgb1/8G0mxACYiIqL3mlwul4rfihUrajodegcKhUIqeo2MjIpUAAOAsbExACAxMRG2trbF7g5RtL0SERERlbKcPr8mJiYazoTeBzl/B+/SF5wFMBEREZUJ7PZAgHr+DlgAExEREZFWYQFMREREVAY4OztjyZIlhY6/c+cOBEFAbGxsieWUW3h4OKysrEplX++KBTARERFRGXDq1CkMGzZMrdssS0WrOnEUCCIiItIKcoWIk/HPkZiSDltzI3hWt4auTtnpV2xjY6PpFNRCT0/z5SdbgImIiKjci7iUgBYLDqLP6r8x/tdY9Fn9N1osOIiISwklts/du3fDysoKCoUCABAbGwtBEDBlyhQpZvjw4ejTpw8AICYmBq1atYKxsTEcHR0xbtw4pKamSrF5u0BcvXoVLVq0gJGREdzc3PDXX39BEATs3LlTKY/bt2+jbdu2MDExgYeHB44fPw4AiIqKwqBBg5CUlARBECAIAubMmQMAyMzMxNSpU+Hg4ABTU1N8+OGHiIqKUtpueHg4qlWrBhMTE3Tr1g3Pnj1762uio6MDW1tb2NraFnkINHViAUxERETlWsSlBIz86SwSktKV5j9OSsfIn86WWBHcqlUrpKSk4Ny5cwCA6OhoVKpUCdHR0VJMVFQUWrdujYsXL8Lb2xvdu3fHhQsXsHnzZhw9ehRjxoxRuW2FQoGuXbvCxMQEJ06cwI8//oiZM2eqjJ05cyb8/f0RGxuLWrVqoU+fPsjKykLz5s2xZMkSWFhYICEhAQkJCfD39wcADBo0CMeOHcOvv/6KCxcuoFevXvDx8cGNGzcAACdOnMDgwYMxatQoxMbGom3btpg3b546X74SxQKYiIiIyi25QkTg7jiIKpblzAvcHQe5QlXEu7G0tETDhg2lltOoqChMnDgR58+fR0pKCh4/fozr16+jTZs2+Pbbb9G3b19MmDABrq6uaN68OZYtW4YNGzYgPT0937b379+PW7duYcOGDfDw8ECLFi3wzTffqMzD398fH3/8MWrVqoXAwEDcvXsXN2/ehIGBASwtLSEIAuzs7GBnZwczMzPcunULv/zyC3777Te0bNkSNWrUgL+/P1q0aIGwsDAAwNKlS+Ht7Y3p06ejVq1aGDduHLy9vdX+GpYUFsBERERUbp2Mf56v5Tc3EUBCUjpOxj8vkf23adMGUVFREEURR44cwaeffor69evj6NGjOHToECpXrow6dergzJkzCA8Ph5mZmfTw9vaGQqFAfHx8vu1eu3YNjo6OsLOzk+Z5enqqzKFBgwbS85zbB+fcTliVs2fPQhRF1KpVSymf6Oho3Lp1CwBw5coVNGvWTGm9vNOqKBQKJCYmIjExUeoaogma74VMREREVEISUwoufosTV1Rt2rTB2rVrcf78eejo6MDNzQ2tW7dGdHQ0Xrx4gdatWwPILgyHDx+OcePG5dtGtWrV8s0TRbHQN4TQ19eXnues86biU6FQQFdXF2fOnMl3q2EzMzNp/8WVlZVV7HXVhQUwERERlVu25kZqjSuqnH7AS5YsQevWrSEIAlq3bo3g4GC8ePEC48ePBwA0btwYly9fRs2aNQu13Tp16uDevXv4559/ULlyZQDZw6QVlYGBAeRyudK8Ro0aQS6XIzExES1btlS5npubG/7++2+leXmn32fsAkFERETllmd1a9hbGqGgtlIBgL1l9pBoJSGnH/BPP/2ENm3aAMguis+ePSv1/wWAadOm4fjx4xg9ejRiY2Nx48YN7Nq1C2PHjlW53Q4dOqBGjRoYOHAgLly4gGPHjkkXwRXlVsHOzs549eoVDhw4gKdPnyItLQ21atVCv3798MUXX2D79u2Ij4/HqVOnsGDBAuzduxcAMG7cOERERCAkJATXr1/HihUrEBERUfwXqpSxACYiIqJyS1dHQEAXNwDIVwTnTAd0cSvR8YDbtm0LuVwuFbsVKlSAm5sbbGxsULduXQDZ/XSjo6Nx48YNtGzZEo0aNcJXX30l9dnNS1dXFzt37sSrV6/wwQcfYOjQoZg1axYAwMio8K3ZzZs3x4gRI/DZZ5/BxsYGISEhAICwsDB88cUXmDx5MmrXro3//e9/OHHiBBwdHQEA//d//4c1a9Zg+fLlaNiwIfbv3y/tvywQxHfpxEFUTiUnJ8PS0hJJSUmwsLDQdDpERFotPT0d8fHxqF69epGKu9wiLiUgcHec0gVx9pZGCOjiBp/6qovMsubYsWNo0aIFbt68iRo1amg6HZUUCgUeP34MALCzsyvWWMBv+nso7Oc3+wATERFRuedT3x4d3OzK9J3g8tqxYwfMzMzg6uqKmzdvYvz48fDy8npvi9/3CQtgIiIi0gq6OgKa1aio6TTUJiUlBVOnTsX9+/dRqVIltG/fHosWLdJ0Wm+Vd2QJTWABTERERFQGffHFF/jiiy80nUaR6OjoSKNWaDQPTSdARERERFSaWAATERERkVZhFwgiIiIiKhUKhQLPnj0DAFSsWLFYo0CoAwtgIiIiIio1MplM0ymwCwQRERERaRcWwERERESkVVgAExEREWmJ8PBwWFlZFWkdX19fdO3atUTyUcXZ2RlLliwp0X2wACYiIiLSEp999hmuX7+u9u2WRtGqTiyAiYiISHuIIvDwTPa/WsjY2Bi2traaTkPjWAATERGR9riwGVj9EXBhS6nsTlXLaMOGDTFnzhwAgCAIWLNmDbp16wYTExO4urpi165dUmyTJk2Ubm/ctWtX6OnpITk5GQDw+PFjCIKAa9euAQAyMzMxdepUODg4wNTUFB9++CGioqKk9VV1gZg3bx5sbW1hbm6OoUOHYvr06WjYsGG+Y1m4cCHs7e1RsWJFjB49WhrNoU2bNrh79y4mTpwIQRAgCIK0TkxMDFq1agVjY2M4Ojpi/PjxSE9Pl4Y/S0xMRJcuXWBsbIzq1atj06ZNRXp9i4sFMBVbmzZtMGHChHfaRlRUFARBwMuXL0ttn1SCynnLilwh4vitZ/g99iGO33oGuaJ8HidRuSXPAg4FZT+PCsqefg8EBgaid+/euHDhAjp37ox+/frh+fPnALI/93IKWFEUceTIEVSoUAFHjx4FABw6dAh2dnaoXbs2AGDQoEE4duwYfv31V1y4cAG9evWCj48Pbty4oXLfmzZtwjfffIMFCxbgzJkzqFatGkJDQ/PFHTp0CLdu3cKhQ4ewfv16hIeHIzw8HACwfft2VK1aFXPnzkVCQgISEhIAABcvXoS3tze6d++OCxcuYPPmzTh27Bi+/vpr2NnZQUdHB76+vrhz5w4OHjyIrVu3YtWqVUhMTFTny6sSC2B6q6IWqUXRvHlzJCQkwNLSslDx27dvx9dffy1Nl7U+R+VeKbeslKaISwloseAg+qz+G+N/jUWf1X+jxYKDiLiUoOnUiKiwLm0FXt7Nfv7iDnBpm0bTyeHr64s+ffqgZs2aCAoKQmpqKk6ePAkguwA+cuQIFAoFLly4AF1dXQwYMEAqiqOiotC6dWsAwK1bt/DLL7/gt99+Q8uWLVGjRg34+/ujRYsWCAsLU7nv5cuXY8iQIRg0aBBq1aqF2bNnw93dPV9chQoVsGLFCtSpUweffPIJPv74Yxw4cAAAYG1tDV1dXZibm8POzg52dnYAgG+//RZ9+/bFhAkT4OrqiubNm2PZsmXYsGED0tPTcf36dfz5559Ys2YNmjVrhiZNmmDt2rV4/fq1ul/ifFgAk0YZGBjAzs5O6eeSN7G2toa5uXkJZ0XF8p62rKhDxKUEjPzpLBKS0pXmP05Kx8ifzrIIJioLpPeonM8bnffmvapBgwbSc1NTU5ibm0utoK1atUJKSgrOnTuH6OhotG7dGm3btkV0dDQA5QL47NmzEEURtWrVgpmZmfSIjo7GrVu3VO772rVr8PT0VJqXdxoA6tWrB11dXWna3t7+rS21Z86cQXh4uFIu3t7eUCgUiI+Px5UrV6Cnp4emTZtK69SpU6fIo1QUBwtgApD9s0pISAhcXFxgbGwMDw8PbN26FXfu3EHbtm0BZH/7EwQBvr6+0noKhQJTp06FtbU17OzspD5NOd7Wt0lV6/KxY8fQunVrmJiYoEKFCvD29saLFy8AKHeBUNXnKDU1FRYWFti6datSHrt374apqSlSUlLU96KRsn9bVjKhh8wXD5AZ+xsyMzOVHllZyh80eZfnfuS9U1BRYmUymdpi5QoRgbvjIALQhQJ6kEvr5nSACNwdx+4QRO87qfU35/+qolRagXV0dCDm6RaW931IX19faVoQBCgUCgCApaUlGjZsiKioKERHR6NNmzZo2bIlYmNjcePGDVy/fh1t2rQBkP2ZrKurizNnziA2NlZ6XLlyBUuXLi0wx7yNUHnzfVuOBVEoFBg+fLhSLufOncOJEydgaWkJuVyucv+lgbdCJgDArFmzsH37doSGhsLV1RWHDx9G//79sW/fPmzbtg09evTAtWvXYGFhAWNjY2m99evXY9KkSThx4gSOHz8OX19feHl5oUOHDlJMYGAgQkJC8O2332L58uXo168f7t69C2tr63x5xMbGol27dhg8eDCWLVsGPT09HDp0SPpPktv27dvh4eGBYcOGwc/PD0D2N+fPP/8cYWFh6NmzpxSbM11Q63FGRgYyMjKk6ZyLC6iQcrWsBAvjsuftuQ7sCVYKc3V1Rd++faXphQsXFnhLTCcnJ6UvW0uXLkVaWprK2CpVqkh/AwCwcuVKJCUlqYy1sbHBqFGjpOnVq1fjyZMnKmMtLS3xYZcBUstvJ8OrsNFJQ9jr/1orRAAJSek4Gf8czWpUVLkdItIwpdbf3MXdv63A9XsAuiVTEtnY2Eh9YoHsz5f4+PgibaNNmzY4dOgQTpw4gblz58LKygpubm7SxWt169YFADRq1AhyuRyJiYlo2bJlobZdu3ZtnDx5EgMGDJDmnT59ukj5Adm/6Ob9rG7cuDEuX76MmjVrSvMUCgUeP34MAKhbty6ysrJw+vRpqdX52rVrJdLlMi+2ABNSU1Px3XffYd26dfD29oaLiwt8fX3Rv39//PDDD1KhamtrCzs7O6X+ug0aNEBAQABcXV3xxRdfoGnTplKfoBxv6tuUV0hICJo2bYpVq1bBw8MD9erVw5gxY1CpUqV8sQX1ORo6dCj27duHR48eAQCePn2KPXv2YPDgwQW+BsHBwbC0tJQejo6ORXsRtV2+lpXyIzEl/e1BRYgjIg0o8D2q5FuBP/roI2zcuBFHjhzBpUuXMHDgQKWuBIXRpk0bREREQBAEuLm5SfM2bdokdX8AgFq1aqFfv3744osvsH37dsTHx+PUqVNYsGAB9u7dq3LbY8eOxdq1a7F+/XrcuHED8+bNw4ULF4rcKuvs7IzDhw/j4cOHePr0KQBg2rRpOH78OEaPHi21WO/atQuzZs0CkF18+/j4wM/PDydOnMCZM2cwdOhQpYa2ksIWYEJcXBzS09OVWm2B7J+cGzVq9MZ1c/dbAlT3CXpT36a8YmNj0atXr6Kkn4+npyfq1auHDRs2YPr06di4cSOqVauGVq1aFbjOjBkzMGnSJGk6OTmZRXBh5WlZmSEu+3eBDmDlCAw/KrWs5Ax7k8Pf37/AzeZ98x0/fnyhY0ePHq3yJzxVsX5+fm+MPX3vv18D/syoA6GAIt/W3KjA/IhIgwps/c1Rsq3AM2bMwO3bt/HJJ5/A0tISX3/9dZFbgHM+v1q3bi29h7Vu3RpLlixRKoCB7F88582bh8mTJ+Phw4eoWLEimjVrhs6dO6vcdr9+/XD79m34+/sjPT0dvXv3hq+vb4ENVQWZO3cuhg8fjho1aiAjIwOiKKJBgwaIjo7GzJkz0bJlS4iiiBo1aqBTp05K+Q4dOhStW7dG5cqVMW/ePHz11VdF2ndxsAAmqQ/PH3/8AQcHB6VlhoaGBXacBwrXJ6go/YbU9a1v6NChWLFiBaZPn46wsDAMGjTojd9mDQ0NYWhoqJZ9a53cV1UDMECufr4vbwHXdwMen6lc1cDAoNC7KUps3r+5d4n1rG4Ne0sjPE5Kh1zFj2YCADtLI3hWz9+lh4jeA/eOK71H5fdvK/C940D1wnUbKAoLCwts3rxZad7AgQOl56q+gOftAmBpaZnvGoquXbsW2Fc3MDAQgYGBKvPx9fVV6l4GAF999ZVS0dmhQwelbgs5w53llncEpv/7v//D+fPn88V98MEH2L9/vzSduwsEANjZ2WHPnj1K6+TujlFSWAAT3NzcYGhoiHv37uX7JgkA9+/fBwCV/XDVrUGDBjhw4ECB/3HzUtXnCAD69++PqVOnYtmyZbh8+bLSmw2pkYZbVkqDro6AgC5uGPnT2XxHmfOVKqCLG3R1Sv8iDiIqBEdPoFc4kJVZcIyeQXacFkpLS8P3338Pb29v6Orq4pdffsFff/2FyMhITadWosrmJxKplbm5Ofz9/TFx4kQoFAq0aNECycnJiImJgZmZGdq3bw9BELBnzx507twZxsbGMDMzK5FcZsyYAXd3d4waNQojRoyAgYEBDh06hF69eqnsB5zT5+jzzz+HoaGhFFOhQgV0794dU6ZMQceOHVG1atUSyVfrabhlpbT41LdHaP/GCNwdpzQUmp2lEQK6uMGnvr0GsyOiN9IzBOp103QW7y1BELB3717MmzcPGRkZqF27NrZt24b27dtrOrUSxQKYAABff/01bG1tERwcjNu3b8PKygqNGzfGl19+CQcHBwQGBmL69OkYNGgQvvjiC5U/h6hDrVq1sH//fnz55Zfw9PSEsbExPvzwQ/Tp00dlvKo+RzmGDBmCn3/++Y0Xv9E70qKWFZ/69ujgZoeT8c+RmJIOW/Psbg9s+SWisszY2Bh//fVXqe5TE8Oe5ctBLOjqD6IybtOmTRg/fjwePXpUpP6jQPZFcJaWlkhKSoKFhUUJZUhERIWRnp6O+Ph4VK9eHUZGvOBU273p76Gwn99sAaZyJy0tDfHx8QgODsbw4cOLXPwSEdH7iW12BKjn74DjAFO5ExISgoYNG6Jy5cqYMWOGptMhIqJ3lDNaS0E3wyHtkvN3UJQRf/JiFwgiFdgFgojo/ZKQkICXL1/C1tYWJiYm70U/Uio6URSlYd6srKyKdB5FUURaWhoSExNhZWUFe/v8FyCzCwQRERGVGzl3+yzoRkpUNoiiKN2q3tLSslhfZKysrKS/h+JiAUxERETvPUEQYG9vD1tbW8hkMk2nQ8WUmZkp3ZZ52LBhRb5OR19fv8i3klaFBTARERGVGbq6umopgEgzdHR0kJqaCgAwMjLS2IXqvAiOiIiIiLQKC2AiIiIi0iosgImIiIhIq7APMJEKOaMDJicnazgTIiKi8iMzMxPp6ekAsj9j1d0HOOdz+22j/HIcYCIVHjx4AEdHR02nQURERMVw//59VK1atcDlLICJVFAoFHj06BHMzc3LzGDrycnJcHR0xP3793nzjvccz1XZwXNVdvBclR0lea5EUURKSgqqVKkCHZ2Ce/qyCwSRCjo6Om/85vg+s7Cw4Jt/GcFzVXbwXJUdPFdlR0mdK0tLy7fG8CI4IiIiItIqLICJiIiISKuwACYqJwwNDREQEABDQ0NNp0JvwXNVdvBclR08V2XH+3CueBEcEREREWkVtgATERERkVZhAUxEREREWoUFMBERERFpFRbARERERKRVWAATvadWrVqF6tWrw8jICE2aNMGRI0feGB8dHY0mTZrAyMgILi4u+P7775WWr169Gi1btkSFChVQoUIFtG/fHidPnizJQ9Aa6j5Xuf36668QBAFdu3ZVc9baqSTO1cuXLzF69GjY29vDyMgIdevWxd69e0vqELRGSZyrJUuWoHbt2jA2NoajoyMmTpyI9PT0kjoErVGUc5WQkIC+ffuidu3a0NHRwYQJE1TGbdu2DW5ubjA0NISbmxt27Nih3qRFInrv/Prrr6K+vr64evVqMS4uThw/frxoamoq3r17V2X87du3RRMTE3H8+PFiXFycuHr1alFfX1/cunWrFNO3b19x5cqV4rlz58QrV66IgwYNEi0tLcUHDx6U1mGVSyVxrnLcuXNHdHBwEFu2bCl++umnJXwk5V9JnKuMjAyxadOmYufOncWjR4+Kd+7cEY8cOSLGxsaW1mGVSyVxrn766SfR0NBQ3LRpkxgfHy/u27dPtLe3FydMmFBah1UuFfVcxcfHi+PGjRPXr18vNmzYUBw/fny+mJiYGFFXV1cMCgoSr1y5IgYFBYl6enri33//rba8WQATvYc8PT3FESNGKM2rU6eOOH36dJXxU6dOFevUqaM0b/jw4eL//d//FbiPrKws0dzcXFy/fv27J6zFSupcZWVliV5eXuKaNWvEgQMHsgBWg5I4V6GhoaKLi4uYmZmp/oS1WEmcq9GjR4sfffSRUsykSZPEFi1aqClr7VTUc5Vb69atVRbAvXv3Fn18fJTmeXt7i59//vk75Zobu0AQvWcyMzNx5swZdOzYUWl+x44dERMTo3Kd48eP54v39vbG6dOnIZPJVK6TlpYGmUwGa2tr9SSuhUryXM2dOxc2NjYYMmSI+hPXQiV1rnbt2oVmzZph9OjRqFy5MurXr4+goCDI5fKSORAtUFLnqkWLFjhz5ozU9ev27dvYu3cvPv744xI4Cu1QnHNVGAWdz3fZZl56atsSEanF06dPIZfLUblyZaX5lStXxuPHj1Wu8/jxY5XxWVlZePr0Kezt7fOtM336dDg4OKB9+/bqS17LlNS5OnbsGNauXYvY2NiSSl3rlNS5un37Ng4ePIh+/fph7969uHHjBkaPHo2srCzMnj27xI6nPCupc/X555/jyZMnaNGiBURRRFZWFkaOHInp06eX2LGUd8U5V4VR0Pl8l23mxQKY6D0lCILStCiK+ea9LV7VfAAICQnBL7/8gqioKBgZGakhW+2mznOVkpKC/v37Y/Xq1ahUqZL6k9Vy6v5/pVAoYGtrix9//BG6urpo0qQJHj16hG+//ZYF8DtS97mKiorCN998g1WrVuHDDz/EzZs3MX78eNjb2+Orr75Sc/bapajnSlPbzI0FMNF7plKlStDV1c33TTcxMTHfN+IcdnZ2KuP19PRQsWJFpfkLFy5EUFAQ/vrrLzRo0EC9yWuZkjhXly9fxp07d9ClSxdpuUKhAADo6enh2rVrqFGjhpqPpPwrqf9X9vb20NfXh66urhRTt25dPH78GJmZmTAwMFDzkZR/JXWuvvrqKwwYMABDhw4FALi7uyM1NRXDhg3DzJkzoaPDXqFFVZxzVRgFnc932WZePNtE7xkDAwM0adIEkZGRSvMjIyPRvHlzles0a9YsX/z+/fvRtGlT6OvrS/O+/fZbfP3114iIiEDTpk3Vn7yWKYlzVadOHVy8eBGxsbHS43//+x/atm2L2NhYODo6ltjxlGcl9f/Ky8sLN2/elL6kAMD169dhb2/P4reYSupcpaWl5StydXV1IWYPCKDGI9AexTlXhVHQ+XyXbeajtsvpiEhtcoaVWbt2rRgXFydOmDBBNDU1Fe/cuSOKoihOnz5dHDBggBSfMwTQxIkTxbi4OHHt2rX5hgBasGCBaGBgIG7dulVMSEiQHikpKaV+fOVJSZyrvDgKhHqUxLm6d++eaGZmJo4ZM0a8du2auGfPHtHW1lacN29eqR9feVIS5yogIEA0NzcXf/nlF/H27dvi/v37xRo1aoi9e/cu9eMrT4p6rkRRFM+dOyeeO3dObNKkidi3b1/x3Llz4uXLl6Xlx44dE3V1dcX58+eLV65cEefPn89h0Ii0xcqVK0UnJyfRwMBAbNy4sRgdHS0tGzhwoNi6dWul+KioKLFRo0aigYGB6OzsLIaGhiotd3JyEgHkewQEBJTC0ZRv6j5XebEAVp+SOFcxMTHihx9+KBoaGoouLi7iN998I2ZlZZX0oZR76j5XMplMnDNnjlijRg3RyMhIdHR0FEeNGiW+ePGiFI6mfCvquVL1WeTk5KQU89tvv4m1a9cW9fX1xTp16ojbtm1Ta87Cv4kQEREREWkF9gEmIiIiIq3CApiIiIiItAoLYCIiIiLSKiyAiYiIiEirsAAmIiIiIq3CApiIiIiItAoLYCIiIiLSKiyAiYiIiEirsAAmIiqCqKgoCIKAly9fltg+2rRpgwkTJpTY9suSO3fuQBAExMbGlqltv8+cnZ2xZMkSafrx48fo0KEDTE1NYWVlVeA8ovKEBTARUR4xMTHQ1dWFj4+PplMpFHUWcomJiRg+fDiqVasGQ0ND2NnZwdvbG8ePH5diBEHAzp0733lfZUGbNm0gCALmz5+fb1nnzp0hCALmzJlT+onlMWfOHAiCAEEQoKenh0qVKqFVq1ZYsmQJMjIylGJPnTqFYcOGSdOLFy9GQkICYmNjcf369QLnEZUnLICJiPJYt24dxo4di6NHj+LevXuaTqdU9ejRA+fPn8f69etx/fp17Nq1C23atMHz5881nVqxZWZmvtP6jo6OCAsLU5r36NEjHDx4EPb29u+0bXWqV68eEhIScO/ePRw6dAi9evVCcHAwmjdvjpSUFCnOxsYGJiYm0vStW7fQpEkTuLq6wtbWtsB5RSWTyd7tgIhKEAtgIqJcUlNTsWXLFowcORKffPIJwsPDVcYdO3YMHh4eMDIywocffoiLFy9Ky+7evYsuXbqgQoUKMDU1Rb169bB3715peXR0NDw9PWFoaAh7e3tMnz4dWVlZBeakqsXVyspKyq169eoAgEaNGkEQBLRp00aKCwsLQ926dWFkZIQ6depg1apVBe7n5cuXOHr0KBYsWIC2bdvCyckJnp6emDFjBj7++GMA2T+fA0C3bt0gCII0fevWLXz66aeoXLkyzMzM8MEHH+Cvv/5S2r6zszOCgoIwePBgmJubo1q1avjxxx+VYk6ePIlGjRrByMgITZs2xblz55SWy+VyDBkyBNWrV4exsTFq166NpUuXKsX4+vqia9euCA4ORpUqVVCrVq1Cbbsgn3zyCZ49e4Zjx45J88LDw9GxY8d8xWFmZiamTp0KBwcHmJqa4sMPP0RUVJS0/NmzZ+jTpw+qVq0KExMTuLu745dfflHaRps2bTBu3DhMnToV1tbWsLOzK1Qrs56eHuzs7FClShW4u7tj7NixiI6OxqVLl7BgwQIpLncXCGdnZ2zbtg0bNmyAIAjw9fVVOQ8AkpKSMGzYMNja2sLCwgIfffQRzp8/L213zpw5aNiwIdatWwcXFxcYGhpCFMVCr7dx40Y4OzvD0tISn3/+uVLRrlAosGDBAtSsWROGhoaoVq0avvnmG2n5w4cP8dlnn6FChQqoWLEiPv30U9y5c+etrxlpLxbARES5bN68GbVr10bt2rXRv39/hIWFQRTFfHFTpkzBwoULcerUKdja2uJ///uf1OI1evRoZGRk4PDhw7h48SIWLFgAMzMzANkf1J07d8YHH3yA8+fPIzQ0FGvXrsW8efOKnfPJkycBAH/99RcSEhKwfft2AMDq1asxc+ZMfPPNN7hy5QqCgoLw1VdfYf369Sq3Y2ZmBjMzM+zcuTPfz+Y5Tp06BSC7sE5ISJCmX716hc6dO+Ovv/7CuXPn4O3tjS5duuRrQV+0aJFUfI4aNQojR47E1atXAWR/+fjkk09Qu3ZtnDlzBnPmzIG/v7/S+gqFAlWrVsWWLVsQFxeH2bNn48svv8SWLVuU4g4cOIArV64gMjISe/bsKdS2C2JgYIB+/foptQKHh4dj8ODB+WIHDRqEY8eO4ddff8WFCxfQq1cv+Pj44MaNGwCA9PR0NGnSBHv27MGlS5cwbNgwDBgwACdOnFDazvr162FqaooTJ04gJCQEc+fORWRkZKHyza1OnTro1KmT9DeR16lTp+Dj44PevXsjISEBS5cuVTlPFEV8/PHHePz4Mfbu3YszZ86gcePGaNeundKvAzdv3sSWLVuwbds2qUtOYda7desWdu7ciT179mDPnj2Ijo5W6nYyY8YMLFiwAF999RXi4uLw888/o3LlygCAtLQ0tG3bFmZmZjh8+DCOHj0KMzMz+Pj4vHPrP5VjIhERSZo3by4uWbJEFEVRlMlkYqVKlcTIyEhp+aFDh0QA4q+//irNe/bsmWhsbCxu3rxZFEVRdHd3F+fMmaNy+19++aVYu3ZtUaFQSPNWrlwpmpmZiXK5XBRFUWzdurU4fvx4aTkAcceOHUrbsbS0FMPCwkRRFMX4+HgRgHju3DmlGEdHR/Hnn39Wmvf111+LzZo1K/D4t27dKlaoUEE0MjISmzdvLs6YMUM8f/68UoyqfFRxc3MTly9fLk07OTmJ/fv3l6YVCoVoa2srhoaGiqIoij/88INobW0tpqamSjGhoaEqjy23UaNGiT169JCmBw4cKFauXFnMyMiQ5hV32znn4vz586K5ubn46tUrMTo6WrS1tRUzMzNFDw8PMSAgQBRFUbx586YoCIL48OFDpW20a9dOnDFjRoH76Ny5szh58mSlfbZo0UIp5oMPPhCnTZtW4DYCAgJEDw8PlcumTZsmGhsbS9NOTk7i4sWLpelPP/1UHDhwoNI6eecdOHBAtLCwENPT05XiatSoIf7www9SDvr6+mJiYmKR1zMxMRGTk5Ol5VOmTBE//PBDURRFMTk5WTQ0NBRXr16t8vjWrl2b7/9URkaGaGxsLO7bt0/lOkR6miy+iYjeJ9euXcPJkyel1jI9PT189tlnWLduHdq3b68U26xZM+m5tbU1ateujStXrgAAxo0bh5EjR2L//v1o3749evTogQYNGgAArly5gmbNmkEQBGl9Ly8vvHr1Cg8ePEC1atXUcixPnjzB/fv3MWTIEPj5+Unzs7KyYGlpWeB6PXr0wMcff4wjR47g+PHjiIiIQEhICNasWSP9FK5KamoqAgMDsWfPHjx69AhZWVl4/fp1vhbgnNcByO7aYWdnh8TERADZr42Hh4dS/9Tcr3OO77//HmvWrMHdu3fx+vVrZGZmomHDhkox7u7uMDAwkKYLu+2CNGjQAK6urti6dSsOHTqEAQMGQF9fXynm7NmzEEVR6nKRIyMjAxUrVgSQ3YVj/vz52Lx5Mx4+fIiMjAxkZGTA1NQ03/5ys7e3l16nohJFUenvrTjOnDmDV69eSceR4/Xr17h165Y07eTkBBsbmyKv5+zsDHNzc2k69/FeuXIFGRkZaNeuXYG53bx5U2l9ILu1Pfc+iHJjAUxE9K+1a9ciKysLDg4O0jxRFKGvr48XL16gQoUKb1w/p8gYOnQovL298ccff2D//v0IDg7GokWLMHbsWJXFiPhvF4uCihRBEPJ1w3jbBUYKhQJAdjeIDz/8UGmZrq7uG9c1MjJChw4d0KFDB8yePRtDhw5FQEDAGwvgKVOmYN++fVi4cCFq1qwJY2Nj9OzZM99P0HmLRkEQpFzzHqMqW7ZswcSJE7Fo0SI0a9YM5ubm+Pbbb/N1IchbUBZm228zePBgrFy5EnFxcVK3k9wUCgV0dXVx5syZfK9xTheYRYsWYfHixViyZAnc3d1hamqKCRMmFOl1KqorV65I/cSLS6FQwN7eXqk/c47cw6Tlfd0Lu96bjtfY2PituTVp0gSbNm3Ktyx3MU6UGwtgIiJkt4xu2LABixYtQseOHZWW9ejRA5s2bcKYMWOkeX///bfUWvvixQtcv34dderUkZY7OjpixIgRGDFiBGbMmIHVq1dj7NixcHNzw7Zt25QK4ZiYGJibmysV3rnZ2NggISFBmr5x4wbS0tKk6ZyWTrlcLs2rXLkyHBwccPv2bfTr16+4LwsAwM3NTekiPH19faV9AcCRI0fg6+uLbt26AcjuE1zUi5Dc3NywceNGvH79Wip6/v7773z7ad68OUaNGiXNK0wrX2G2/f/t3V9I02scx/G34oVKFwkSZky6mMpCsc0LKWiKCHaTExOMKRLOaChhZH+QXEE3amiReiMYSnijgdAfhkMm1EDSxvBPmRgxIckliHjVTdG5EH64k5IdPRfn7POC3ez3+z3Ps8HGZ9/f8zz7HafTyfXr18nLy+PEiRO/HLdarfz48YO1tTXOnDmzYxuBQACHw0FNTQ2wFd4+fvyIxWL5o7Hs1eLiImNjY7S0tOyrHZvNRiQSISEhwVj4+G9et11mZiZJSUn4/X7q6+t37GN4eNhYZCeyF1oEJyICvHz5ko2NDVwuFzk5OVGPyspKHj9+HHX+vXv38Pv9vHv3josXL5Kamkp5eTkAV69exefzEQ6HCYVCTExMGAGnoaGBz58/c+XKFRYXF3n27Bl3797l2rVrxMfv/JVcXFxMb28voVCIYDCI2+2OqpgdOXKEpKQkxsbG+Pr1K5ubm8DW6vq2tjYePXrE0tIS8/PzDAwM8ODBgx37WV9fp7i4mKGhIebm5giHwzx9+pT79+/jcDiM844fP47f7ycSibCxsQGA2WxmdHSUmZkZZmdncTqdf1yxdDqdxMfH43K5WFhYwOv10tnZGXWO2WwmGAzi8/lYWlrC4/EYC/H22/bvpKSksLq6it/v3/F4VlYW1dXV1NbWMjo6Sjgc5u3bt3R0dBi7gJjNZsbHx5mcnOTDhw9cvnyZSCTyR+PYzffv34lEInz58oX5+Xl6enooLCzk5MmT3LhxY19tl5SUcOrUKcrLy/H5fCwvLzM5OUlrayvBYPDAr9suMTGRW7ducfPmTZ48ecKnT5948+aN8Zmsrq4mNTUVh8NBIBAgHA7z6tUrmpqaWFlZ2dfrlv8vBWAREbamP5SUlOw4P/b8+fPMzMwQCoWM59rb22lqaiI/P5/V1VWeP38eVYltbGzEYrFw9uxZsrOzje3Hjh07htfrZXp6mry8PNxuNy6Xi9bW1l3H1tXVhclkwm63G1XI7XNZExIS6O7upq+vj/T0dCOs1tfX09/fz+DgILm5uRQWFjI4OLjr7fBDhw5RUFDAw4cPsdvt5OTk4PF4uHTpEr29vVHjGR8fx2QyYbVaga0/TkhJSeH06dOcO3eO0tJSbDbbXt9+o/8XL16wsLCA1Wrl9u3bUdt3AbjdbioqKqiqqqKgoID19fWoavB+2t6Lw4cP/3Kbf7uBgQFqa2tpbm4mOzubsrIypqamMJlMAHg8Hmw2G6WlpRQVFZGWlmb8cNqv9+/fc/ToUTIyMigqKmJkZISWlhYCgYAxBeOfiouLw+v1YrfbqaurIysriwsXLrC8vGzsxnCQ1/2dx+OhubmZO3fuYLFYqKqqMuYIJycn8/r1azIyMqioqMBisVBXV8e3b99UEZZdxf08iIlRIiIiIiL/EaoAi4iIiEhMUQAWERERkZiiACwiIiIiMUUBWERERERiigKwiIiIiMQUBWARERERiSkKwCIiIiISUxSARURERCSmKACLiIiISExRABYRERGRmKIALCIiIiIx5S8W3k8Duc2ycAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "res.plot_covariate_balance(kind=\"love\", phase=\"valid\", thresh=0.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Weighted이후에 |SMD|가 더 줄어든 것을 보았을 때, Weighted 이후 밸런스가 개선되었음을 확인할 수 있습니다." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### IPW 가중치 분포 및 유효표본수(ESS)" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ESS: 10354.681665498 (N: 10391 )\n" - ] - } - ], - "source": [ - "w = weight_model.compute_weights(X, T)\n", - "ESS = (w.sum()**2) / (w**2).sum()\n", - "print(\"ESS:\", float(ESS), \" (N:\", len(w), \")\")" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'IPW weights (log y)')" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(); plt.hist(w, bins=40); plt.yscale('log'); plt.title('IPW weights (log y)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1) ESS와 N수가 거의 비슷한 것을 확인할 수 있음 -> 가중치가 고르게 퍼져있다\n", - "2) 히스토그램이 1 주변에 모여있고(= 가중치 과도하게 쏠리지 않음), 꼬리 부분도 완만(= 극단 가중치 거의 없음)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Propensity Score Overlap\n", - "아래는 처음에 Logistic Regression으로 추정한 Propensity Score의 Distribution을 나타낸 것입니다." - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_propensity_score_distribution(\n", - " propensity=data_ps['propensity_score'],\n", - " treatment=data_ps['intervention'],\n", - " reflect=False,\n", - " kde=False,\n", - " norm_hist=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "하지만 이는 하나의 데이터에 적합 및 예측을 동시에 하기 때문에 Overlap이 실제보다 좋아보일 수 있습니다." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "그렇다면 OOF 폴드 예측으로 얻은 PS 분포를 그려봅시다. \n", - "(cv = None -> 단일 적합, cv = \"auto\" -> 교차검증)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "res = evaluate(weight_model, X, T, Y, cv=\"auto\")\n", - "res.plot_weight_distribution(phase=\"valid\", reflect=False, norm_hist=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "OOF로 검증해도 Propensity Score의 Overlap 및 Positivity가 양호한 것을 확인" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ROC Curve" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "res.plot_roc_curve(phase=\"valid\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "위의 그래프는 우리가 구한 Propensity Score의 Overlap 정도를 확인하기 위한 ROC Curve이다(AUC가 0.5에 가까울수록 as-if random). \n", - "- Propensity: 가중치 없이 Propensity Score 자체만으로 보았을 때\n", - "- Weighted: IPW 가중을 걸었을 때 -> 만약 Propensity Score와의 차이가 크면 극단 PS에 의해 분산 및 불안정성이 커진 신호\n", - "- Expected: 무작위 배정 레퍼런스 -> 위 두 경우의 AUC가 Expected와 크게 다르면 PS가 과도하게 극단이거나 표본 불균형 존재\n", - "\n", - "그래프 해석: Facure의 책에서 Treatment가 완전 무작위가 아니라고 밝혔다. 하지만, 우리가 잡은 \"X 기준으로 보았을 때(Given X)\"는 ROC Curve로 본 Treatment는 거의 랜덤에 준한다고 판단할 수 있다." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Double Machine Learning (비모수 버전의 Regression 처럼 활용 가능)" - ] } ], "metadata": { "kernelspec": { - "display_name": "Python (bayes311)", + "display_name": "base", "language": "python", - "name": "bayes311" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2590,9 +47,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.13" + "version": "3.12.7" } }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 2 }