diff --git a/lib/data.py b/lib/data.py index 6ed2a35..b49ca48 100644 --- a/lib/data.py +++ b/lib/data.py @@ -82,3 +82,28 @@ def load_data(path: Path): movies = movies[~movies.averageRating.isna()].copy() return movies + + +def load_rating_train_dev_test(movies: pd.DataFrame, train_max_year=2015, dev_max_year=2017, sample_count: int = None): + """ + :param movies: Movies dataframe + :param train_max_year: cut year for training + :param dev_max_year: cut year for dev (and starts test) + :param sample_count: whether to take a sample (useful for testing the code). Ignored when it is None + """ + if sample_count: + movies = movies.sample(sample_count) + + train_df = movies[movies.startYear <= train_max_year] + dev_df = movies[(movies.startYear > train_max_year) & (movies.startYear <= dev_max_year)] + test_df = movies[movies.startYear > dev_max_year] + + X_train = train_df.to_dict(orient='records') + X_dev = dev_df.to_dict(orient='records') + X_test = test_df.to_dict(orient='records') + + y_train = train_df.averageRating.values + y_dev = dev_df.averageRating.values + y_test = test_df.averageRating.values + + return dict(X_train=X_train, y_train=y_train, X_dev=X_dev, y_dev=y_dev, X_test=X_test, y_test=y_test) diff --git a/lib/model.py b/lib/model.py new file mode 100644 index 0000000..a444d2c --- /dev/null +++ b/lib/model.py @@ -0,0 +1,28 @@ +from . import transformers +from sklearn.pipeline import make_union, make_pipeline +from sklearn.feature_extraction import DictVectorizer + + +def get_features_pipe( + use_years: bool, use_genre: bool, + use_director: bool, director_kws: dict = None, post_processing=None): + steps = [] + if use_years: + steps.append(make_pipeline(transformers.YearsAgo(), DictVectorizer(sparse=False))) + + if use_genre: + steps.append(make_pipeline(transformers.GenreDummies(), DictVectorizer(sparse=False))) + + if use_director: + director_kws = director_kws or {} + # cuando hacemos **director_kws usamos ese diccionario para pasar parametros + steps.append(make_pipeline(transformers.DirectorFeatures(**director_kws), DictVectorizer(sparse=False))) + + res = make_union(*steps) + if post_processing: + res = make_pipeline(res, post_processing) + return res + + +def get_model_pipe(features_pipe, model): + return make_pipeline(features_pipe, model) \ No newline at end of file diff --git a/lib/transformers/__init__.py b/lib/transformers/__init__.py index 9ba235e..1a88dcc 100644 --- a/lib/transformers/__init__.py +++ b/lib/transformers/__init__.py @@ -1,3 +1,3 @@ -from .director_features import DirectorFeatures +from .director_features import CrewFeatures, DirectorFeatures from .genre_dummies import GenreDummies from .years_ago import YearsAgo \ No newline at end of file diff --git a/lib/transformers/director_features.py b/lib/transformers/director_features.py index 9638237..1ed8f2b 100644 --- a/lib/transformers/director_features.py +++ b/lib/transformers/director_features.py @@ -2,8 +2,9 @@ import pandas as pd -class DirectorFeatures(BaseEstimator, TransformerMixin): - def __init__(self, min_cnt_movies=2): +class CrewFeatures(BaseEstimator, TransformerMixin): + def __init__(self, field, min_cnt_movies=2): + self.field = field self.min_cnt_movies = min_cnt_movies def fit(self, X, y): @@ -11,33 +12,30 @@ def fit(self, X, y): # Llevamos las cosas de nuevo a un DataFrame y calculamos features por director directors_stats = ( pd.DataFrame(X) - .groupby('director') - .agg({ - 'tconst': 'count', - 'averageRating': ['mean', 'max', 'min'], - 'numVotes': ['mean', 'min', 'max']} + .groupby(self.field) + .agg( + n_films=('tconst', 'count'), + min_rating=('averageRating', 'min'), + avg_rating=('averageRating', 'mean'), + max_rating=('averageRating', 'max'), + min_votes=('numVotes', 'min'), + avg_votes=('numVotes', 'mean'), + max_votes=('numVotes', 'max'), ) ) - # Para hacer flattening de las columnas - # https://stackoverflow.com/questions/14507794/pandas-how-to-flatten-a-hierarchical-index-in-columns - directors_stats.columns = [ - '_'.join(i) - for i in zip(directors_stats.columns.get_level_values(1), directors_stats.columns.get_level_values(0)) - ] - # Guardamos las estadisticas self.directors_stats_ = directors_stats # Diccionario con los datos para los directores comunes self.directors_stats_lk_ = ( - directors_stats[directors_stats.count_tconst >= self.min_cnt_movies].to_dict(orient='index') + directors_stats[directors_stats.n_films >= self.min_cnt_movies].to_dict(orient='index') ) # Valor default para los que consideramos que tenemos demasiado poca data - self.default_ = directors_stats[directors_stats.count_tconst < self.min_cnt_movies].mean(0).to_dict() + self.default_ = directors_stats[directors_stats.n_films < self.min_cnt_movies].mean(0).to_dict() if self.min_cnt_movies > 1: - self.default_ = directors_stats[directors_stats.count_tconst < self.min_cnt_movies].mean(0).to_dict() + self.default_ = directors_stats[directors_stats.n_films < self.min_cnt_movies].mean(0).to_dict() else: self.default_ = directors_stats.mean(0).to_dict() return self @@ -45,8 +43,14 @@ def fit(self, X, y): def transform(self, X): res = [] for e in X: - if e['director'] in self.directors_stats_lk_: - res.append(self.directors_stats_lk_[e['director']]) + if e[self.field] in self.directors_stats_lk_: + res.append(self.directors_stats_lk_[e[self.field]]) else: res.append(self.default_) - return res \ No newline at end of file + return res + + +# Para retrocompatibilidad del material en el curso +class DirectorFeatures(CrewFeatures): + def __init__(self, min_cnt_movies=2): + super().__init__(field='director', min_cnt_movies=min_cnt_movies) diff --git a/notebooks/clase-1/01_get_the_data.ipynb b/notebooks/clase-1/01_get_the_data.ipynb index 092ebb7..f2c96b6 100644 --- a/notebooks/clase-1/01_get_the_data.ipynb +++ b/notebooks/clase-1/01_get_the_data.ipynb @@ -87,38 +87,36 @@ "outputs": [], "source": [ "# descargamos los datos\n", - "!wget https://machine-learning-practico.s3.amazonaws.com/aclImdb_v1.tar.gz -O $DATA_HOME/aclImdb_v1.tar.gz\n", - "!wget https://machine-learning-practico.s3.amazonaws.com/movie_gross.csv -O $DATA_HOME/movie_gross.csv\n", - "!wget https://machine-learning-practico.s3.amazonaws.com/name.basics.tsv.gz -O $DATA_HOME/name.basics.tsv.gz\n", - "!wget https://machine-learning-practico.s3.amazonaws.com/title.akas.tsv.gz -O $DATA_HOME/title.akas.tsv.gz\n", - "!wget https://machine-learning-practico.s3.amazonaws.com/title.basics.tsv.gz -O $DATA_HOME/title.basics.tsv.gz\n", - "!wget https://machine-learning-practico.s3.amazonaws.com/title.crew.tsv.gz -O $DATA_HOME/title.crew.tsv.gz\n", - "!wget https://machine-learning-practico.s3.amazonaws.com/title.principals.tsv.gz -O $DATA_HOME/title.principals.tsv.gz\n", - "!wget https://machine-learning-practico.s3.amazonaws.com/title.ratings.tsv.gz -O $DATA_HOME/title.ratings.tsv.gz" + "!wget https://datasets.imdbws.com/name.basics.tsv.gz -O $DATA_HOME/name.basics.tsv.gz\n", + "!wget https://datasets.imdbws.com/title.akas.tsv.gz -O $DATA_HOME/title.akas.tsv.gz\n", + "!wget https://datasets.imdbws.com/title.basics.tsv.gz -O $DATA_HOME/title.basics.tsv.gz\n", + "!wget https://datasets.imdbws.com/title.crew.tsv.gz -O $DATA_HOME/title.crew.tsv.gz\n", + "!wget https://datasets.imdbws.com/title.principals.tsv.gz -O $DATA_HOME/title.principals.tsv.gz\n", + "!wget https://datasets.imdbws.com/title.ratings.tsv.gz -O $DATA_HOME/title.ratings.tsv.gz" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "Wl0qAm-hboiQ" - }, + "metadata": {}, "outputs": [], "source": [ - "# descomprimimos \n", - "!ls $DATA_HOME/*.gz | grep -v aclImdb_v1.tar.gz | xargs -I% gunzip \"%\"" + "# PARA DESCARGAR movie_gross.csv bajalo a mano de acá\n", + "# https://drive.google.com/file/d/1Aav7imwH7s1U2W3Olwgyd1tzUcYGUtcu/view?usp=sharing" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Wl0qAm-hboiQ" + }, "outputs": [], "source": [ - "# Opcional, no lo usamos en la materia, toma mucho tiempo en descomprimir\n", - "!tar -C $DATA_HOME -vxf $DATA_HOME/aclImdb_v1.tar.gz" + "# descomprimimos \n", + "!ls $DATA_HOME/*.gz | xargs -I% gunzip \"%\"" ] } ], diff --git a/notebooks/clase-3/01-rating-distribution.ipynb b/notebooks/clase-3/01-rating-distribution.ipynb new file mode 100644 index 0000000..37a57c4 --- /dev/null +++ b/notebooks/clase-3/01-rating-distribution.ipynb @@ -0,0 +1,461 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from google.colab import drive\n", + "\n", + "drive.mount('/content/gdrive')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Llevamos el repositorio a nuestro drive" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Esto solo debemos ejecutarlo una vez. Si lo ejecutamos mas de una vez va a fallar (pero no pasa nada!)\n", + "!mkdir /content/gdrive/My\\ Drive/ml-practico/code\n", + "# Acá podes usar tu propio repo si queres hacer modificaciones (highly recommended)\n", + "!git clone https://github.com/elsonidoq/machine_learning_practico /content/gdrive/My\\ Drive/ml-practico/code" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Si hay cambios en el repositorio, con esta linea actualizas tu drive\n", + "!cd /content/gdrive/My\\ Drive/ml-practico/code; git checkout clase-3; git pull" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('/content/gdrive/My Drive/ml-practico/code')" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "# Para trabajar local\n", + "sys.path.append('/Users/przivic/prog/machine_learning_practico')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loading the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from lib import model, data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "# Para trabajar en colab\n", + "PATH = Path('/content/gdrive/My Drive/ml-practico/data/')\n", + "\n", + "# Para trabajar local\n", + "PATH = Path('../../data/')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading title basics...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/przivic/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3343: DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading title ratings...\n", + "Loading movie directors...\n" + ] + } + ], + "source": [ + "movies = data.load_data(PATH)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_df = movies[movies.startYear <= 2017]\n", + "test_df = movies[movies.startYear > 2017]\n", + "\n", + "len(train_df), len(test_df), len(test_df) / len(train_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y_train = train_df.averageRating\n", + "y_test = test_df.averageRating\n", + "\n", + "X_train = train_df.to_dict(orient='records')\n", + "X_test = test_df.to_dict(orient='records')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modelling per se" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Armamos todos los problemas de clasificacion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "thresholds = np.linspace(1.5, 9.5, 15)\n", + "thresholds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# El punto medio entre cada par de threshold, util para graficar\n", + "mids = [(t0 + t1)/2 for t0, t1 in zip(thresholds[:-1], thresholds[1:])]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_bools(y):\n", + " res = []\n", + " for t in thresholds:\n", + " res.append(y <= t)\n", + " return res\n", + "\n", + "ys_train = get_bools(y_train)\n", + "ys_test = get_bools(y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Armamos los modelos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "models = [\n", + " model.get_model_pipe(\n", + " features_pipe=model.get_features_pipe(\n", + " use_years=False, use_director=True, use_genre=True, post_processing=StandardScaler()\n", + " ),\n", + " model=LogisticRegression() \n", + " )\n", + " for _ in range(len(thresholds))\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Entrenamos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i, m in enumerate(models):\n", + " print(i)\n", + " m.fit(X_train, ys_train[i])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualizamos los modelos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from random import randint" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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7iMGDB9fYp7ugoIDly5dzzTW/jO1cVFTkdtlJkybRrFkzwLpA7N5772Xt2rUEBwezdetWj2KaMGEC4eHhhIeH06FDBw4dOkRGRgaTJ0+u2PZll13m0bZU07Ir7xSLt+Tyu9/0IXJzKGEhQXyx/qAm+gDgn4m+miPvxtSiRYuK2yEhIZSVlVXcL+/vXVZWRps2bSp+CXi6vWeeeYaYmBjWrVtHWVkZERERHsUUHh5ecTs4OJiSkhJ88aI45XvmLs8mNFi4fkh3zsyFqBZhLNmaS0FRCS3D/TNVKIu20XsoMjKSkydPVjk/JiaGw4cPk5+fT1FRUUXTSqtWrYiLi+Pdd63B6Y0xrFu3rsbHO378OJ06dSIoKIg33nij4oRvTXG4k56ezqeffkphYSEFBQV8/vnntVpfBb7SMsN7WTlMTOpMh0jroCKqRRhnS8pYvPmww9Gp+tJE76F27doxYsQIEhISeOCBB86ZHxoaymOPPcaQIUOYOHEi/fr1q5g3b948Zs+ezcCBAxkwYAAff/xxjY83Y8YM5s6dy9ChQ9m6dWvF0X5SUhIhISEMHDiQZ555xqPY09LSmDRpEgMHDuTKK68kNTWV1q1be/jMVVOQW1BEQVEJtw6PrZgWGRFK+5bhLNpw0LnAlFf4Ta2bTZs20b9/f4ci8n8FBQW0bNmS06dPM2rUKGbOnElKSorTYdWa7gf19Jo9JtDUX37VTV00lXV7j9Gj+I98OGMEALtvuhmAmVc+wAdr9rHmzxfTLCy40cNVnguIWjeqfqZNm0ZycjIpKSlcddVVfpnkVcM4drqYM8WlvzqaL3dpYifOFJfy3VZtvvFneoaliXC9yEopVwdPFBIaEsT4hE7nzBsSF0Xb5qF8seEg49zMV/5Bj+iVasJ25hZw7PRZYiIjCAs5Nx2EBAcxNr4j32w6TGGxd68AV41HE71STdjrK3YTJEJMq/Aqlxmf2JGCohIytuU1YmTKmzTRK9VEnSws5r2sHKJahBEaXHUqGN6rPa0iQvhCe9/4LU30SjVR72flUFBUQsfW1V+MFxYSxG/iY/j654OcLSmrdlnlmzTRK4+4Fk/75JNPePLJqq9Orlzpc//+/Vx99dUNHqPyXFmZYe6K3ZzfvY1HV71emtCJE4UlLN+hzTf+SBO9w7xd4rgxHn/SpEk8/PDDVc6vnOg7d+7Me++9V6f4VMP4flsuu/JOue1S6U56n/a0DA/Ri6f8lCb6Wrj88ssZNGgQAwYMYObMmQC8+OKLPPjggxXLzJkzh/vuuw+AN998k8GDB5OcnMxdd91VkVRbtmxZcRXtihUreOKJJ0hLSyMhIYFp06ZV1KbJzMwkKSmJYcOG8cADD1SUQS4tLeWBBx4gLS2NpKQkXn755XNizc7Opl+/ftxyyy0kJSVx9dVXc/r0aQBiY2N54oknSE9P59133+Wrr75i2LBhpKSkcM0111BQUADAokWL6NevH+np6XzwwQe/eo733nsvAIcOHeKKK65g4MCBDBw4kOXLl59T0tm1hHNhYSFTp04lMTGR888/n8WLF1ds88orr2TcuHH06dPnV6+p8r45dpVKd10q3YkIDeaifh34cuNBSkq1+cbf+GU/+qdWPfWrEXG8oV9UPx4a/FC1y7z66qtERUVx5swZ0tLSuOqqq7j66qsZNmwY//znPwF45513+NOf/sSmTZt45513WLZsGaGhocyYMYN58+Zx8803c+rUKRISEnjiiScAiI+P57HHHgPgpptu4rPPPuOyyy5j6tSpzJw5k+HDh//qCHr27Nm0bt2azMxMioqKGDFiBGPHjj2nkuaWLVuYPXs2I0aM4LbbbuOFF17gj3/8IwARERFkZGSQl5fHlVdeyX/+8x9atGjBU089xb/+9S8efPBB7rzzTr799lt69+7Ntdde6/Y1uf/++xk9ejQffvghpaWlFBQUnFPS2bWE8/PPPw/A+vXr2bx5M2PHjq2ozLl27Vp+/PFHwsPD6du3L/fddx/dunXz6P1TnjtTXMqSnbn8/jfnue1SWZVLEzvyybr9/LDrCCN6t2/ACJW36RF9LTz33HMMHDiQoUOHsnfvXrZt20Z0dDQ9e/Zk5cqV5Ofns2XLFkaMGME333xDVlYWaWlpJCcn880337Bz507Aqip51VVXVWx38eLFDBkyhMTERL799ls2btzIsWPHOHnyJMOHDwfg+uuvr1j+q6++4vXXXyc5OZkhQ4aQn5/Ptm3bzom3W7dujBhhXdJ+4403kpGRUTGvPHGvXLmSn3/+mREjRpCcnMzcuXPZvXs3mzdvJi4ujj59+iAi3HjjjW5fk2+//Za777674nnVVEMnIyODm266CYB+/frRo0ePikQ/ZswYWrduTUREBPHx8ezevbvabam6OXSisKJKZW2MPq8DzUKD+WLDgQaKTDUUvzyir+nIuyEsWbKE//znP6xYsYLmzZtzwQUXVJQivvbaa1mwYAH9+vXjiiuuQEQwxnDLLbfwj3/845xtRUREEBxs1Q0pLCxkxowZrF69mm7duvH4449TWFhYbWlhYwz//ve/ueSSS6qNWUSqvF9eJM0Yw8UXX8zbb7/9q2XXrl17zvreUN3zcldiWXlXSVkZuSeLuCypM9GRVfedd6dZWDAX9otm0YZD/PekBIKDvL9/qIahR/QeOn78OG3btqV58+Zs3ryZlStXVsy78sor+eijj3j77bcrjpTHjBnDe++9x+HDVo2QI0eOuD1CLf+yaN++PQUFBRUnLdu2bUtkZGTF48yfP79inUsuuYQXX3yR4mJrmLetW7dy6tSpc7a9Z88eVqxYAcDbb79Nenr6OcsMHTqUZcuWsX37dgBOnz7N1q1b6devH7t27WLHjh0V67szZswYXnzxRcA6d3DixIlqSymPGjWKefPmVcS9Z88e+vbt63ZZ5X15BUWUGsMtHp6ErWx8QifyCopYnX3Eu4GpBqWJ3kPjxo2jpKSEpKQk/vznPzN06NCKeW3btq1oahg8eDBgtbv/9a9/ZezYsSQlJXHxxRdz4MC5P3nbtGnDnXfeSWJiIpdffjlpaWkV82bPns20adMYNmwYxpiKZpE77riD+Ph4UlJSSEhI4K677nJ79Nu/f3/mzp1LUlISR44cqWhicRUdHc2cOXOYMmUKSUlJDB06lM2bNxMREcHMmTOZMGEC6enp9OjRw+3r8uyzz7J48WISExMZNGgQGzdurLak84wZMygtLSUxMZFrr72WOXPm/OpIXjWcsjLDweNFtAwPYWC3NnXaxoX9OhAeEqQXT/kZj8oUi8g44FkgGJhljHmy0vwbgPL2lALgbmPMOnteNnASKAVKqiqj6UrLFFvKSwuDNWbtgQMHePbZZz1aNzs7m4kTJ7Jhw4aGDLHRNcX9wFsWbzlMs3mT6B3dkvb3/adi+tRFUwF4bdxrFdPKyxT3eOP1c7Zz5+urWZ9znOUPX0SQNt/4jHqVKRaRYOB5YDwQD0wRkfhKi+0CRhtjkoC/ADMrzb/QGJPsSZJXv/j8889JTk4mISGBpUuX8uijjzodkvJj1lCBQUS1CKvXdi5N7MjBE4X8uPeYlyJTDc2Tk7GDge3GmJ0AIjIfmAz8XL6AMWa5y/IrAR1N2AuuvfbaKrs11iQ2NjbgjuZV3e3MLWDJllz+GhNOUD1Pso/pH0NosLBowwEG9WjrpQhVQ/Kkjb4LsNflfo49rSq3A1+43DfAVyKSJSLTqlpJRKaJyGoRWZ2bm+tBWEopT72+YjdhwUEV48HWR6uIUNJ7t2fh+oM68Lyf8CTRu/v6d/vuisiFWInetf/jCGNMClbTzz0iMsrdusaYmcaYVGNManR0tAdhKaU8cbKwmHdX72ViUifCqqlSWRvjEzux79gZ1u877pXtqYblybueA7hentgV2F95IRFJAmYBk40x+eXTjTH77f+HgQ+xmoKUUo3kvawcTp0trXOXSnfGxscQEiTa+8ZPeJLoM4E+IhInImHAdcAnrguISHfgA+AmY8xWl+ktRCSy/DYwFtCGY6UaSVmZ4fUVu0np3qbOXSrdadM8jGG92vHF+gPafOMHakz0xpgS4F7gS2ATsMAYs1FEpovIdHuxx4B2wAsislZEyvtGxgAZIrIOWAV8boxZ5PVn4YcCsezvrbfeqlUqfcx3dpVKbx7Nlxuf0Ins/NNsOuD+4jjlOzwqgWCMWQgsrDTtJZfbdwB3uFlvJzCwnjH6ldLS0oryBp6aNGkSkyZNqnJ+eaKfMWMGoGV/lefmLMumQy2qVNbG2AExPPrRehZtOEB851Ze377yHr0y1kOBWvY3NjaWRx55hGHDhpGamsqaNWu45JJL6NWrFy+9ZH2XG2MqyiQnJibyzjvvVEy/9957iY+PZ8KECRXlHgCysrIYPXo0gwYN4pJLLnF7VbBqWDtyC/huay43Du1RqyqVnmrfMpzBcVEs1HZ6n+eXRc0O/v3vFG3ybpni8P796PjII9UuE6hlf7t168aKFSv4/e9/z6233sqyZcsoLCxkwIABTJ8+nQ8++IC1a9eybt068vLySEtLY9SoUaxYsYItW7awfv16Dh06RHx8PLfddhvFxcXcd999fPzxx0RHR1eUbn711Vdr/b6ounvD7lI5ZXDtqlTWxqWJnXjs441sO3SSPjGRDfY4qn70iL4WArXsb3mzUWJiIkOGDCEyMpLo6GgiIiI4duwYGRkZTJkyheDgYGJiYhg9ejSZmZl8//33FdM7d+7MRRddBFhfiBs2bODiiy8mOTmZv/71r+Tk5Hj0GivvcO1SWdsqlbVxyYCOiKC9b3ycXx7R13Tk3VACtexv+XJBQUG/WicoKIiSkpJqH8PdczLGMGDAgIrKmarxNUSXSndiWkUwqHtbFq4/wP1j+jToY6m60yP6WmiqZX9HjRrFO++8Q2lpKbm5uXz//fcMHjyYUaNGMX/+fEpLSzlw4EDF+YG+ffuSm5tb8VoVFxezceNGr8akqlZWZpi7PNvrXSqrMj6xE5sPnmRX3rmlspVv0ERfC0217O8VV1xBUlISAwcO5KKLLuKf//wnHTt25IorrqBPnz4kJiZy9913M3r0aADCwsJ47733eOihhxg4cCDJycksX768hkdR3vLdtlyy809z64i4mhf2gnEJHQF05Ckf5lGZ4sbmi2WKA7Xsr79xej/wB7e8uopNB06w7OGLCK1c8uC1Cdb/qZ9XTKptmWJ3Jj+/jLIyw6f3nfsrVzWOepUpVkr5D9culeck+QZ0aUJH1u87zt4jpxvtMZXnNNF7SMv+Kn/w+vLsBu9S6U75BVmLtPeNT/KrRO+LzUyq8ej7X72ThcW8l5XDxIEN26XSne7tmjOgcysWaju9T/KbRB8REUF+fr5+2JsoYwz5+flERNS/nnqgKu9SeWsDd6msyqWJnfhxzzEOHD/jyOOrqvlNP/quXbuSk5ODDkrSdEVERNC1qw5e5o5rl8qkrg3fpdKd8Qkd+Z8vt7Bow0GmNlKPH+UZv0n0oaGhxMXpzqOUO99ttbpU/mGsd6+hqI2e0S3pGxPJF+s10fsav2m6UUpV7bXl2cS0Cme83afdKeMTO5K5+wiHTxY6Gof6NU30Svm5HbkFfL81lxuHNG6XSncuTeyEMfDlxkOOxqF+TRO9Un6uokvlkMbtUulOnw4t6Rndgi/Wa+8bX6KJXik/dsKlS2X7lo3bpdIdEeHShE6s3JlPfkGR0+EomyZ6pfzYe6utLpVTh/vOyc/xiR0pM/D1z9p84ys00Svlp6yBv7MZ1KMtiV2rH7egMcV3akX3qOY68pQP0USvlJ8q71LZ0DXna0tEGJ/YkeXb8zh+utjpcBSa6JXyW77SpdKdSxM6UVJm+HqTNt/4Ak30SvkhX+pS6U5S19Z0adNMe9/4CN/bQ5RSNXpz5W6f6VLpjogwLqEjS7flcbJQm2+cpoleKT/0zabDjDqvvU90qazKpYkdOVtaxrebDzsdSpOniV4pP7M7/xR7jpxm1HnRTodSrfO7tSWmVTgLtfnGcR4lehEZJyJbRGS7iDzsZv4NIvKT/bdcRAZ6uq5Sqna+35YHwMg+vp3og4KEcQM6smRLLqeKSpwOp0mrMdGLSDDwPDAeiAemiHS0ZwsAAB5LSURBVEh8pcV2AaONMUnAX4CZtVhXKVULS7fm0qVNM2LbNXc6lBqNT+xEUUkZS7ZoeXEneXJEPxjYbozZaYw5C8wHJrsuYIxZbow5at9dCXT1dF2llOdKSstYsSOfUee1R0ScDqdGabFRtG8ZpiNPOcyTRN8F2OtyP8eeVpXbgS9qu66ITBOR1SKyWgcXUcq9dTnHOFlU4vPNNuWCg4SxAzqyePNhCotLnQ6nyfIk0bs7bHA7np+IXIiV6B+q7brGmJnGmFRjTGp0tH/sxEo1tu+35hEkMLxXO6dD8dilCZ04fbaU77bqAZxTPEn0OUA3l/tdgf2VFxKRJGAWMNkYk1+bdZVSnlm6LZekrm1o0zzM6VA8NqRnFG2ah+rFUw7yJNFnAn1EJE5EwoDrgE9cFxCR7sAHwE3GmK21WVcp5ZnjZ4pZu/cYI/u0dzqUWgkNDmJsfAzfbDpMUYk23zihxkRvjCkB7gW+BDYBC4wxG0VkuohMtxd7DGgHvCAia0VkdXXrNsDzUCrgrdiRT5nx/W6V7oxP7MTJohKWbc9zOpQmyaPBwY0xC4GFlaa95HL7DuAOT9dVStXe0m25tAgL5vzubZwOpdZG9GpPZEQIC9cf5KJ+MU6H0+TolbFK+Yml2/IY1qu9TxYxq0lYSBAX94/h658PUVxa5nQ4TY7/7TFKNUHlZQ/8rX3e1fjEThw/U8yKHfk1L6y8ShO9Un7gl7IH/pvoR/ZpT4uwYL7Qi6canSZ6pfxAxjar7EFc+xZOh1JnEaHBXNQ/hi83HqJEm28alSZ6pXxcSWkZy7f7T9mD6lya0JEjp86yKvuI06E0KZrolfJx/lb2oDqj+0YTERrEZz9p801j0kSvlI/7fmse4mdlD6rSPCyESxM78dGP+zh+Rkeeaiya6JXycRnb8/yu7EF1bhsRx+mzpcxftcfpUJoMTfRK+bDysgej/Li3TWUJXVozrGc75i7P1j71jUQTvVI+bMWOfErLTEC0z7u6PT2O/ccL+WLDQadDaRI00Svlw/y57EF1LurXgbj2LZi9dCfGuK1crrxIE71SPswqe9DOL8seVCcoSLgtPY51OcfJ2n205hVUvQTW3qNUAPml7EFgNduUuyqlC62bhTJr6S6nQwl4muiV8lFLA6DsQXWah4Vww5DufPnzQfbkn3Y6nICmiV4pH7U0AMoe1OSW4bGEBAmvLdej+oakiV4pH1Re9mBkH/8ve1CdmFYRTEzqzILMvXoBVQPSRK+UDwqksgc1uT09jlNnS3knUy+gaiia6JXyQUu3WWUPRvT2/7IHNUno0pqhPaOYsyxbq1o2EE30SvmgpdsCq+xBTW5P76kXUDUgTfRK+ZhALHtQkzH9OhDbrjmzMnbpBVQNQBO9Uj6mvOxBeu+mk+iDgoTb0+NYt/cYa/boBVTepoleKR/zS9mDtk6H0qiuGtRVL6BqIJrolfIxGdutsgdhIU3r49k8LITrh3Tny40H2XtEL6Dypqa1Jynl43bnn2J3fuCWPajJLcNiCRLhtWXZTocSUDTRK+VDyssepDehE7GuOraOYGJSJ97J3MOJQr2Ayls8SvQiMk5EtojIdhF52M38fiKyQkSKROSPleZli8h6EVkrIqu9FbhSgai87EHPAC57UJPb03taF1Ct2ut0KAGjxkQvIsHA88B4IB6YIiLxlRY7AtwPPF3FZi40xiQbY1LrE6xSgayktIzlOwK/7EFNEru2ZkhcFHOW6wVU3uLJEf1gYLsxZqcx5iwwH5jsuoAx5rAxJhPQ31pK1dG6nOOcLGwaZQ9qcnt6HPuOnWHRRr2Ayhs8SfRdANffUDn2NE8Z4CsRyRKRabUJTqmmZOm23CZT9qAmY/rHWBdQaVdLr/Ak0bv7DVmbS9dGGGNSsJp+7hGRUW4fRGSaiKwWkdW5ubm12LxSgWHptjySurRuMmUPqhNsj0C1du8xHYHKCzxJ9DlAN5f7XYH9nj6AMWa//f8w8CFWU5C75WYaY1KNManR0frTVTUtJwqtsgfabPOLq1K60ioihNkZO50Oxe95kugzgT4iEiciYcB1wCeebFxEWohIZPltYCywoa7BKhWoysseBOpoUnXRIjyE64f0YNEGvYCqvmpM9MaYEuBe4EtgE7DAGLNRRKaLyHQAEekoIjnAH4BHRSRHRFoBMUCGiKwDVgGfG2MWNdSTUcpfNdWyBzW5ZXgPgkSYszzb6VD8WognCxljFgILK017yeX2QawmncpOAAPrE6BSTcHSbU2z7EFNOrVuxoSkTryTuZff/aYPkRGhTofkl3SvUsph5WUPmlK1ytq4PT2OgqIS3snUC6jqShO9Ug4rL3sw8jw9EetOUtc2DI6L4jUdgarONNEr5bCMbXlNvuxBTcovoPpy4yGnQ/FLmuiVclBJaRnLduQ1+bIHNflN/xh6tGuuXS3rSBO9Ug4qL3vQVKtVeio4SLhtRBxr9ugFVHWhiV4pB1WUPeilib4mVw+yLqB6NUPLItSWJnqlHJRhlz1o20LLHtSkRXgIU4Z054sNB/QCqlrSRK+UQ04UFvOjlj2olVuHWyNQzdULqGpFE71SDtGyB7XXqXUzLk3sxPzMvZzUEag8poleKYcs3ZZLcy17UGt3jLQuoFqwOsfpUPyGJnqlHLJ0Wx7DemrZg9pK6tqGwbFRvLZsl15A5SHdw5RywJ780+zOP63NNnV0W3ocOUfP8NXPegGVJzTRK+WApdutwXW07EHdXBwfQ/eo5szWrpYe0USvlAOWbtWyB/VhXUAVS9buo6zZoxdQ1UQTvVKNrLzsQXpvLXtQH9ekdiMyIkSP6j2giV6pRlZe9mDkedo+Xx8twkO4fnB3Fm04SM5RvYCqOprolWpkGdvytOyBl9wyPBZAL6CqgSZ6pRrZ0m25WvbASzq3sS+gWrWXgqISp8PxWZrolWpE5WUPtFql99yeHsfJohIW6AhUVdJEr1Qj+qXsgXar9Jbkbm1Ii23Lq8t2UVpmnA7HJ2miV6oRZWzLo3lYMCla9sCrbi+/gGrjQadD8Uma6JVqREu35WrZgwZwcXxHukU1066WVdC9TalGsif/NNla9qBBlI9AtXr3UX7UC6jOoYleqUZSXvYgXdvnG8Q1qd2IDNcLqNzRRK9UI1m6NY/OrSPoFa1lDxpCy4oRqA7qCFSVaKJXqhGUlJaxfEceI/tEa9mDBjR1RCxBAq8s3el0KD7Fo0QvIuNEZIuIbBeRh93M7yciK0SkSET+WJt1lWoKftp3nBNa9qDBdWrdjCvP78o7mXvJPVnkdDg+o8ZELyLBwPPAeCAemCIi8ZUWOwLcDzxdh3WVCnhLt2rZg8Zy1+ienC0t47Vl2lZfzpMj+sHAdmPMTmPMWWA+MNl1AWPMYWNMJlB5EMca11WqKVi6LZdELXvQKHpGt+TShE68sWI3J3RcWcCzRN8FcL22OMee5gmP1xWRaSKyWkRW5+bmerh5pXxfedkD7VbZeO6+oBcni0p4c+Vup0PxCZ4kendnjjy9ztjjdY0xM40xqcaY1Oho7X6mAsdKLXvQ6BK6tGbUedG8mrGLwuJSp8NxnCeJPgfo5nK/K7Dfw+3XZ12lAsJSLXvgiBkX9CKv4CzvrtZiZ54k+kygj4jEiUgYcB3wiYfbr8+6SgWEpdtyGaplDxrdkLgoUrq34eXvd1JSWuZ0OI6qcc8zxpQA9wJfApuABcaYjSIyXUSmA4hIRxHJAf4APCoiOSLSqqp1G+rJKOVrtOyBc0SEGRf0JufoGT79qWk3JIR4spAxZiGwsNK0l1xuH8RqlvFoXaWaivKyB9o+74yL+nWgb0wkLy7ZweSBXQgKapoXq+lvSaUaUMY2LXvgpKAg4e4LerH1UAHfbD7sdDiO0USvVAMpKS1j2XYte+C0iUmd6BbVjBeWbMeYpjkwiSZ6pRpIedkDHTbQWSHBQUwb1Ysf9xxj5c4jTofjCE30SjWQirIHvTXRO+2aQV1p3zKcF5ZsdzoUR2iiV6qBZGy3yh5EadkDx0WEBnN7ehxLt+WxPue40+E0Ok30SjWA9TnHWbPnGOl6NO8zbhzanciIkCZ5VK+JXikv23zwBDe9+gOdWkdwy/BYp8NRtsiIUG4e1oNFGw+y/XCB0+E0Kk30SnnRjtwCbpz1AxEhwbx1x1BiWkU4HZJyMXVEHGHBQbz83Q6nQ2lUmuiV8pK9R05zwys/APDmHUPo3q65wxGpytq3DOe6tG58+OM+9h8743Q4jUYTvVJecOD4Ga6ftZIzxaW8cfsQendo6XRIqgp3juoJNK3hBjXRK1VPuSeLuGHWDxw9Vczrtw2mf6dWToekqtG1bXMmJXdm/qq9HDl11ulwGoUmeqXq4djps9w0+wcOHCvktalpDOzWxumQlAfuHt2LM8WlzGkiww1qoleqjk4UFnPzq6vYmXeKV25OJS02yumQlIf6xEQyNj6GOcuzKSgqcTqcBqeJXqk6OH22hNtey+Tn/Sd48YYULXPgh2Zc2JsThSW89UPgDzeoiV6pWiosLmXa61ms2XOUZ687nzH9Y5wOSdVBcrc2DO/VjllLd1FUEtjDDWqiV6oWzpaUcc+8NWRsz+N/rh7IhKROToek6mHGBb05fLKI97P2OR1Kg9JEr5SHSkrL+P07a/lm82H+enkCVw1yO9aO8iMjercjqWtrXv5+R0APN6iJXikPlJUZHnz/Jz5ff4BHJ/TnxqE9nA5JeYE13GAvduefZuGGg06H02A00StVA2MMf/54Ax+s2ccfLj6PO0b2dDok5UVj4zvSK7oFLy7ZEbADk2iiV6oaxhj+9vkm5v2wh+mje3HfRb2dDkl5mTXcYG82HTjBki25TofTIDTRK1WNZ77eyqyMXdw6PJaHxvXVIQED1OTkznRp0yxgSxhroleqCi8u2cFz327n2tRuPDYxXpN8AAsNDuLOkXFkZh8lMzvwhhvURK+UG3OW7eKpRZuZnNyZv1+ZSFCQJvlAd21ad9q1COOFxYF3VK+JXqlKFmTu5fFPf2ZsfAxPXzOQYE3yTUKzsGCmjohl8ZZcft5/wulwvEoTvVIuPl67j4c++InR50Xz7+vPJzRYPyJNyU3DYmkZHsKLATYwiUd7sYiME5EtIrJdRB52M19E5Dl7/k8ikuIyL1tE1ovIWhFZ7c3glfKmRRsO8ocF6xgcG8VLNw4iPCTY6ZBUI2vdLJQbhnbn85/2k513yulwvKbGRC8iwcDzwHggHpgiIvGVFhsP9LH/pgEvVpp/oTEm2RiTWv+QlfK+JVsOc9/ba0jq2prZt6bRLEyTfFN1e3ocIcFBvPx94AxM4skR/WBguzFmpzHmLDAfmFxpmcnA68ayEmgjIloERPmFFTvyueuNLM6LiWTO1MG0DA9xOiTloA6REVwzqCvvZ+Vw6ESh0+F4hSeJvguw1+V+jj3N02UM8JWIZInItKoeRESmichqEVmdmxuYFy0o37Nmz1Fun5tJ96jmvHH7EFo3C3U6JOUD7hrVi5KyMmZnBMbAJJ4kenddDipfJ1zdMiOMMSlYzTv3iMgodw9ijJlpjEk1xqRGR0d7EJZS9bN27zFueXUVHSLDmXfHEKJahDkdkvIR3ds157KBnZm3cjfHTxc7HU69eZLoc4BuLve7Avs9XcYYU/7/MPAhVlOQUo766Md9XPvyClo3C2XenUPp0CrC6ZCUj7n7gl6cOlvK3BXZTodSb54k+kygj4jEiUgYcB3wSaVlPgFutnvfDAWOG2MOiEgLEYkEEJEWwFhggxfjV6pWSssM/1i4id+9s5bkbm34+J4RdGnTzOmwlA/q17EVY/p14LVluzh91r+HG6wx0RtjSoB7gS+BTcACY8xGEZkuItPtxRYCO4HtwCvADHt6DJAhIuuAVcDnxphFXn4OSnnk+JlibpuTycvf7+SmoT14844htGsZ7nRYyofNuLAXR08XM3/V3poX9mEedS8wxizESuau015yuW2Ae9ystxMYWM8Ylaq37YcLmPb6avYcOc3fr0jk+iHdnQ5J+YFBPaIYHBfFK0t3cuPQHoSF+OcFdP4ZtVK1sHjzYa54fhnHzxTz1p1DNcmrWplxQS8OHC/ko7X+O9ygJnoVsIwxvLhkB7fNzaR7u+Z8cl86g+OinA5L+ZnR50UzoHMrXvpuB6Vl/jkwiSZ6FZDOnC3lt/PX8tSizUxI7MR704frSVdVJyLC3Rf0YmfuKb7a6J/DDWqiVwFn/7EzXPPycj79aT8PXNKXf085X0saqHoZn9CJuPYteH7Jdr8cblATvQooq7OPMOl/l5Gdd5pZN6dyz4W9dcAQVW/BQcJdo3qyYd8JFvnhIOKa6FXAmL9qD1NeWUnL8GA+umc4Y/rHOB2SCiBXpHShb0wk97y1hucXb6fMj9rrNdErv1dcWsZjH2/g4Q/WM7RnOz6+J53eHSKdDksFmPCQYN6fMZwJSZ35ny+3cOucTPILipwOyyOa6JVfO3LqLDfPXsXrK3Zz58g4Xrs1jdbNtTCZahgtw0N47rpk/nZFAit35jPhuQy/GGNWE73yW5sPnmDS/2aQteco//eagfxpQjwhOiKUamAiwg1DevDB3cOJCA3iupkreem7HT7dlKOfCuWXFm04wJUvLOdsSRkL7hrGVYO6Oh2SamISurTm0/vSGTegI09+sZk7Xl/N0VNnnQ7LLU30yq+UlRme+Xor099cw3kxkXx6XzrJ3do4HZZqoiIjQvnf68/nickDyNiWx4TnlpK1+6jTYZ1DE73yG6eKSrh7XhbPfrONq1K6Mn/aUGK0vLBymIhw87BY3rt7GMHBwrUvr+CV73f6VH97TfTKL+zJP82VLyzn658P8eeJ8Tx9TRIRoXoRlPIdSV3b8Nl9IxnTvwN/W7iJO1/P8plBSzTRK5+3fHsek57P4OCJQubeNpjb0+P0Iijlk1o3C+WlGwfx2MR4vtt6mEufW8ravcecDksTvfJNpWWGDfuO88zXW7np1VVEtwzn43tGMLKPDjOpfJuIcFt6HO9OHw7ANS8t59WMXY425ehw98onFBaX8lPOcTKzj7Bq1xGydh+loMga1WdsfAz/9/8MJDJC+8cr/5HcrQ2f35/OH99dxxOf/cyqXUd46uokRwag10SvHHGysJis3UfJzD5C5q6jrM05xtmSMgD6dGjJpOTODImLIi02is5adVL5qTbNw3jl5lRmLd3FU4s2c9m/M3j++hQSu7Zu1Dg00atGkVdQROauI6yyj9g3HThBmbGKRSV0bsUtw3qQFhtFamwUUS3CnA5XKa8REe4c1ZOUHm259601XPXich6d2J+bhvZotHNNmuiV1xljyDl6hlW7jlQ0xezMOwVAeEgQKd3bcu9FfRgcG8X53dvQIlx3QxX4BvVoy8L7R/KHBWt57OON/LDrCE9emdgoTZL6CVP1VlZm2Ha4oOJoPXPXEQ6eKASgVUQIabFR/J+0bqTFRpHYpbXfjrupVH21bRHG7FvSePn7nTz91RY27jvO8zekMKBzwzblaKJXtXaqqIR1Ocf4cc8xsnYfZc2eoxyz+wvHtAonLdYaUDktNoq+MZEEBWlXSKXKBQVZI1alxlpNOVe8sJz/77J4rh/cvcGacjTRq2qVN8Os2XO0IqlvOnCyYuzM3h1aMjY+hrTYKIbEtaNbVDPt466UB9Jio1h4/0h+v2Adf/pwAz/sPMLfr0ykZQM0ZWqiV79SWFzKxv3HraS++xhZe46Se9Kqud08LJjkbm2YcUEvUnq05fxubWjTXE+cKlVX7VqGM+fWNF5Ysp1/fb2VDfuP8+m96V4/b6WJvok7dKKQNbuto/WsPUfZuO8EZ0utbo7do5qT3rs9Kd3bkNKjLX1jIrUMsFJeFhQk3HtRHwb1iCIz+0iDdE7QRN+EFJeWsfnAyYpmmKzdR9l37AwAYSFBJHVpzdQRsaT0aEtK97ZER4Y7HLFSTcewXu0Y1qtdg2xbE70fMMZwtrSMwuIyCotLOXO2lDPF1l/h2VIKS0o5c7bsV9Mqbtt/u/JOsW7vcc4UlwLWSdPUHlFMHRHLoB5tGdBZe8MoFag8SvQiMg54FggGZhljnqw0X+z5lwKngVuNMWs8WdcfGWMoLjUUllhJtMhOwIXFZRXTCovLKCoprUjOhcWlFJX8cvuc+SVlLkn7lyRdfrsug9eEBgsRocE0Cw2mY+sIrk3rRkqPtgzq0ZbOrSP0pKlSTUSNiV5EgoHngYuBHCBTRD4xxvzssth4oI/9NwR4ERji4bpeM3/VHgqLSykps46AS0oNxaVlFNv/S0rLOFtqKCktc1nml/nF7tYpM5wtKaOkrIyzJWUVCbquo4aJQERIMOGhQUSEBBMRGkREaDDhocE0Cw2iXYswmrUNtuaFWUm6WWgwzcKCK5J2s7Agt/ObhVrLlG8zVNvTlVJ4dkQ/GNhujNkJICLzgcmAa7KeDLxurPJsK0WkjYh0AmI9WNdr/vvTnyuaJsqJQGhwEKFBQmhIECFBQYQFCyHBQYQGizUvOIgQ+3Z4aBAtI0Ks5UKEkKAgexlrfnkSjQgNJjzE3W37v53Ew8v/2/PCgoP0SFop1ag8SfRdgL0u93OwjtprWqaLh+sCICLTgGkA3bt39yCsc3334AV2Yv4lgQfrxTpK/aJj4jmT+kX1O2daeP9zpyn/5Umid5cpKzdcVLWMJ+taE42ZCcwESE1NrVPDSIdIHVZOqWqNP/cU2UODHzpnWsdHHmmMaFQj8STR5wDdXO53BfZ7uEyYB+sqpZRqQJ6crcsE+ohInIiEAdcBn1Ra5hPgZrEMBY4bYw54uK5SSqkGVOMRvTGmRETuBb7E6iL5qjFmo4hMt+e/BCzE6lq5Hat75dTq1m2QZ6KUUsotcXIcw6qkpqaa1atXOx2GUkr5DRHJMsakupunHa2VUirAaaJXSqkAp4leKaUCnCZ6pZQKcD55MlZEcoHdDfgQ7YG8Bty+N2iM9efr8YHG6C0aI/QwxkS7m+GTib6hicjqqs5O+wqNsf58PT7QGL1FY6yeNt0opVSA00SvlFIBrqkm+plOB+ABjbH+fD0+0Bi9RWOsRpNso1dKqaakqR7RK6VUk6GJXimlAlyTSfQi0k1EFovIJhHZKCK/dTqmykQkQkRWicg6O8b/djqmqohIsIj8KCKfOR2LOyKSLSLrRWStiPhkhTx7yM33RGSzvV8OczomVyLS1379yv9OiMjvnI6rMhH5vf152SAib4uIz41AJCK/tePb6MRr2GTa6O0xbDsZY9aISCSQBVzeUAOV14VYg8m2MMYUiEgokAH81hiz0uHQziEifwBSgVbGmIlOx1OZiGQDqcYYn72IRkTmAkuNMbPs8RqaG2OOOR2XOyISDOwDhhhjGvJixloRkS5Yn5N4Y8wZEVkALDTGzHE2sl+ISAIwH2v87bPAIuBuY8y2xoqhyRzRG2MOGGPW2LdPApuwxrT1GcZSYN8Ntf987ptYRLoCE4BZTsfir0SkFTAKmA1gjDnrq0neNgbY4UtJ3kUI0ExEQoDm+N4odv2BlcaY08aYEuA74IrGDKDJJHpXIhILnA/84Gwk57KbRNYCh4GvjTE+FyPw/4AHgTKnA6mGAb4SkSx74Hlf0xPIBV6zm8BmiUgLp4OqxnXA204HUZkxZh/wNLAHOIA1ut1XzkZ1jg3AKBFpJyLNsQZp6lbDOl7V5BK9iLQE3gd+Z4w54XQ8lRljSo0xyVjj6w62f/b5DBGZCBw2xmQ5HUsNRhhjUoDxwD0iMsrpgCoJAVKAF40x5wOngIedDck9u1lpEvCu07FUJiJtgclAHNAZaCEiNzob1a8ZYzYBTwFfYzXbrANKGjOGJpXo7Xbv94F5xpgPnI6nOvbP+CXAOIdDqWwEMMluA58PXCQibzob0rmMMfvt/4eBD7HaR31JDpDj8ovtPazE74vGA2uMMYecDsSN3wC7jDG5xphi4ANguMMxncMYM9sYk2KMGQUcARqtfR6aUKK3T3TOBjYZY/7ldDzuiEi0iLSxbzfD2ok3OxvVrxlj/ssY09UYE4v1c/5bY4xPHUGJSAv7hDt2c8hYrJ/PPsMYcxDYKyJ97UljAJ/pGFDJFHyw2ca2BxgqIs3tz/gYrPNvPkVEOtj/uwNX0sivZ42DgweQEcBNwHq7DRzgEWPMQgdjqqwTMNfu4RAELDDG+GT3RR8XA3xofe4JAd4yxixyNiS37gPm2U0jO4GpDsdzDrtN+WLgLqdjcccY84OIvAeswWoO+RHfLIfwvoi0A4qBe4wxRxvzwZtM90qllGqqmkzTjVJKNVWa6JVSKsBpoldKqQCniV4ppQKcJnqllApwmuiVUirAaaJXSqkA9/8DbjU39qPslBoAAAAASUVORK5CYII=\n", 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\n", 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yxz/+kUGDBnH55ZfTo8d/20zfeust5s2bR2JiIr179+bjjz+u8fVmzZrF/PnzGTx4MDt37qys7SckJBAQEEBiYiJPP/20S7EPGDCAiRMnkpiYyNVXX01KSgphYWEuvnPlDjKOneKrrUe5eWgMzYN+eiA+e3Q3ThWXMn95pj3BKbfnlnPGpqSkmKoTj2zbto2ePXvaFJHnKygooHnz5hQVFTFixAjmzp1LcnKy3WHVWuXn4F8TrCeme9/RyfQvpwPwr/H/qnzuvvc28ummQyx/cDQRzYPYN3UaANFvvA7AbfPXkL7vJGkPjj7nh0D5BhFZa4xJqW6Z1uh9xIwZM0hKSiI5OZlrrrnGI5O8rzqYe5qP1h/k+gGdiWgeVG2Zu0fHkVtUwhsrzzvJkPJh+tPvI5wvslKe5ZVlewC4bfj5e3gldmrJyO5RvLJsDzcPjSakiX611X9pjV4pN5ZTeJaFqw8wMak9HVuFXLDsPWO6kV14lrd/2N9I0SlPoYleKTf22opMTpeUMXNk1xrL9o8OJ7VbBC8v3UNxSf0Oy6E8myZ6pdxU4ZlS5q/IZGyvNsS1CXVpnbtHx3H81BkWrtZavfovTfRKuakFq/eTd7qEmaNqrs1XGNwlgoGx4bz0/R7OlGqtXlk00SuXOA+e9sknn/D44+e/OrnqSJ+HDh3i2muvbfAYvUm5Mfxz2R4GdwknuXOrWq17z+g4juQX8156VgNFpzyNJnqb1fcQx43x+hMnTuShh84/6UfVRN++fXsWLVp0UfH5qhMFZzmaf4aZo7rVet3UbhEkd27Ji0t2c7a0vOYVlNdzKdGLyHgR2SEiGSJyzjdcRG4UkU2O2woRSXR1XU9y5ZVX0r9/f3r37s3cuXMBePHFF3nggQcqy7z22mvcfffdALz55psMHDiQpKQk7rjjjsqk2rx588qraFeuXMmjjz7KgAED6NOnDzNmzKgcm2bNmjUkJCQwZMgQ7r///sphkMvKyrj//vsZMGAACQkJvPzyy+fEmpmZSY8ePbj55ptJSEjg2muvpaioCICYmBgeffRRhg0bxnvvvcfXX3/NkCFDSE5O5rrrrqOgoACAL7/8kh49ejBs2DA++OCDn7zH2bNnA3D06FGuuuoqEhMTSUxMZMWKFecM6ew8hHNxcTHTp0+nb9++9OvXj8WLF1du8+qrr2b8+PHExcX9ZJ/6GgMczj1N7/YtGBEXWev1RYS7x8RxMPc0H67XWr1yoR+9iPgDLwBjgSxgjYh8Yoz50anYXmCkMeakiFwGzAUGubhurT2x+okLzsBzMXqE9+DBgQ9esMyrr75KeHg4p0+fZsCAAVxzzTVce+21DBkyhCeffBKAd955h9/97nds27aNd955h+XLlxMYGMisWbN46623mDZtGoWFhfTp04dHH30UgF69evHHP/4RgKlTp/Lpp59yxRVXMH36dObOncvQoUN/UoOeN28eYWFhrFmzhjNnzpCamsq4cePOGUlzx44dzJs3j9TUVG699VbmzJnDfffdB0BwcDBpaWmcOHGCq6++mm+++YZmzZrxxBNP8Pe//50HHniA22+/ne+++45u3boxefLkavfJPffcw8iRI/nwww8pKyujoKDgnCGdnYdwfuGFFwDYvHkz27dvZ9y4cZUjc27YsIH169cTFBREfHw8d999N506dXLp/+dNThaetXrajOp60fPkjuoeRULHMF5YvJtrkjsS4K8H777Mlf/+QCDDGLPHGHMWWAhMci5gjFlhjDnpeLgK6Ojqup7k2WefJTExkcGDB3PgwAF27dpFVFQUXbp0YdWqVWRnZ7Njxw5SU1P59ttvWbt2LQMGDCApKYlvv/2WPXusC1/8/f255pprKre7ePFiBg0aRN++ffnuu+/YunUrubm5nDp1iqFDhwJwww03VJb/+uuvef3110lKSmLQoEFkZ2eza9euc+Lt1KkTqampANx0002kpaVVLqtI3KtWreLHH38kNTWVpKQk5s+fz759+9i+fTuxsbHExcUhItx0003V7pPvvvuOmTNnVr6vmsbQSUtLY+rUqQD06NGD6OjoykQ/ZswYwsLCCA4OplevXuzb53tXeRpjOJh7muBAfy7r0+6ityMi3D06jv05RXy84VA9Rqg8kSuXz3UADjg9zgIGXaD8L4AvaruuiMwAZgB07ty5uiKVaqp5N4QlS5bwzTffsHLlSkJCQhg1alTlUMSTJ0/m3XffpUePHlx11VWICMYYbr75Zv72t7+ds63g4GD8/f0Bqylj1qxZpKen06lTJx555BGKi4svOLSwMYbnnnuOSy+99IIxV60NOj+uGCTNGMPYsWNZsGDBT8pu2LDhomuTF3Kh91XdEMu+ZnlGNoVnSukS1Rx/v7rt/5/1bE3Pdi14YXEGV/brUOftKc/lSo2+uk9Htd9WEbkEK9FXZGKX1zXGzDXGpBhjUqKiolwIq3Hl5eXRqlUrQkJC2L59O6tWrapcdvXVV/PRRx+xYMGCyprymDFjWLRoEceOHQMgJyen2hpqxY9FZGQkBQUFlSctW7VqRWhoaOXrLFy4sHKdSy+9lBdffJGSEmtY2p07d1JYWHjOtvfv38/KlSsBWLBgAcOGDTunzODBg1m+fDkZGRkAFBUVsXPnTnr06MHevXvZvXt35frVGTNmDC+++CJgnTvIz8+/4FDKI0aM4K233qqMe//+/cTHx1db1hfNWZJBkwA/IpvXfRIREeGe0d3Yc6KQTzdprd6XuZLoswDnhtKOwDmfGhFJAF4BJhljsmuzricYP348paWlJCQk8Ic//IHBgwdXLmvVqlVlU8PAgQMBq939scceY9y4cSQkJDB27FgOHz58znZbtmzJ7bffTt++fbnyyisZMGBA5bJ58+YxY8YMhgwZgjGmslnktttuo1evXiQnJ9OnTx/uuOOOamu/PXv2ZP78+SQkJJCTk1PZxOIsKiqK1157jSlTppCQkMDgwYPZvn07wcHBzJ07lwkTJjBs2DCio6Or3S/PPPMMixcvpm/fvvTv35+tW7decEjnWbNmUVZWRt++fZk8eTKvvfbaT2ryvmzDgVxW7M6mXVgwfvV0NHVp77Z0b9Oc57/LoLzc/UaqVY2jxmGKRSQA2AmMAQ4Ca4AbjDFbncp0Br4DphljVtRm3eroMMWWiqGFwZqz9vDhwzzzzDMurZuZmcnll1/Oli1bGjLERufNwxTf8UY6K3dnkzRwAf5+8pNhiquqOkzxhXyy8RD3LFjPnBuT+Xnfi2/3V+6tTsMUG2NKgdnAV8A24F1jzFYRuVNE7nQU+yMQAcwRkQ0ikn6hdev8jnzEZ599RlJSEn369GHZsmX8/ve/tzsk1UCcJxap77b0CX3b0SWqGc99l6HTSvool8YyNcZ8Dnxe5bmXnO7fBtzm6rrKNZMnTz5vt8aaxMTEeF1t3pu9/P0eggP9uGVoDPel1Vy+Nvz9hNmXdOPedzfyzbZjjO3Vpn5fQLk97VyrlM0O5Z7mwxomFqmriYntiY4I4dlvd2mt3gdpolfKZq8s2wtceGKRugrw9+OuUd3YfDCPJTuPN9jrKPekiV4pG+UUnmXB6v0uTSxSV1cld6BDy6Zaq/dBmuiVstF8x8Qid7owsUhdBfr7MeuSrqzfn8vyjOyaV1BeQxO9Tbxx2N9bbrlFR6mshcIzpby2IpOf9WxDdxcnFqmra/t3pF1YMM9+e+6QGcp7aaKvZzrsr3JVxcQisy5p+Np8haAAf+4c2ZXVmTms2qO1el+hid5F3jrsb0xMDA8//DBDhgwhJSWFdevWcemll9K1a1deesnqQWuMqRwmuW/fvrzzzjuVz8+ePZtevXoxYcKEyuEeANauXcvIkSPp378/l156abVXBfuys6XlvLJsL4Niaz+xSF1NHtCJqNAgrdX7EJf60bubI3/9K2e21e8wxUE9e9D24YcvWMZbh/3t1KkTK1eu5Ne//jW33HILy5cvp7i4mN69e3PnnXfywQcfsGHDBjZu3MiJEycYMGAAI0aMYOXKlezYsYPNmzdz9OhRevXqxa233kpJSQl33303H3/8MVFRUZVDN7/66qu1/r94q4/WH+RIfjFPXJvQ6K8dHOjPHSO68Nhn20jPzCElJrzRY1CNS2v0teCtw/5OnDgRgL59+zJo0CBCQ0OJiooiODiY3Nxc0tLSmDJlCv7+/rRp04aRI0eyZs0ali5dWvl8+/btGT16NGD9IG7ZsoWxY8eSlJTEY489RlaWToBRoazc8NLS3Rc9sUh9uHFQNBHNmvDsdxm2vL5qXB5Zo6+p5t1QvHXY34pyfn5+P1nHz8+P0tLSC75Gde/JGEPv3r0rR85UP/X11iPsOV7I8zf0a5DPhCuaNvHn9hFdePyL7Ww4kEtSp5a2xKEah9boa8FXh/0dMWIE77zzDmVlZRw/fpylS5cycOBARowYwcKFCykrK+Pw4cOV5wfi4+M5fvx45b4qKSlh61Yd4gisH8EXv99NdERInSYWqQ83DY6mZUggz2lbvdfTRF8Lvjrs71VXXUVCQgKJiYmMHj2aJ598krZt23LVVVcRFxdH3759mTlzJiNHjgSgSZMmLFq0iAcffJDExESSkpJYsWJFDa/iG5ZnZLMpK487RnS1fSKQ5kEB/CI1lm+3H2PLwTxbY1ENq8Zhiu3gjsMUe+uwv57G04cpvvGVVew6WsCyBy8hKMC/2jLTv5wOUG/DFF9IfnEJqY9/x9CuEbw8tdoRbpWHqNMwxUqp+rHxgHVF6i+GxZ43yTe2FsGBTE+N5autR9l+JN/ucFQD0UTvIh32V9XVi0t20yI4gBsGXXhO5MZ2a2oMzYMCeE574Hgtj0r07tjMpBqPJ///M44V8NWPR5g2JIbQ4EC7w/mJliFNmDYkms83HybjWPUn/ZVn85hEHxwcTHZ2tkd/2dXFM8aQnZ1NcHCw3aFclJe/301QgB/TU2PsDqVatw3vQtNAf57XWr1X8ph+9B07diQrK4vjx3UsbV8VHBxMx44d7Q6j1g7lnuajDQe5YWDDTSxSV+HNmnDT4GheWbaHX/6sO7GRzewOSdUjj0n0gYGBxMY23MQMSjWUeWl7KTdWrdmd3T68C/NXZPLC4gyeui7R7nBUPfKYphulPNFJx8QikxLb0ym8YScWqauo0CBuGNSZD9cf5EBOkd3hqHqkiV6pBjR/ZSZFZ8u4c1TjDUVcF3eOtC7kmrNE2+q9iSZ6pRqIHROL1FWbFsFMTunEorVZHM47bXc4qp5ooleqgSxcc4DcohJmekhtvsKtw2IpKTN8vfWo3aGoeqKJXqkGUHimlFeW7WFQbDj9oxt3YpG6io1sRmxkM77fqT3cvIUmeqXqmTGGBxZt4mh+MfeO7W53OBdlZPcoVuw+QXFJ7afGVO5HE71S9ezlpXv4bPNhHhjfg0FdIuwO56KMjI+iuKScNZk5doei6oEmeqXq0bJdx3nyy+1M6NuOO0a4d7/5CxkcG0GTAD+W7NDmG2+giV6penIgp4i7F6wnrnUoT16bYNvsUfWhaRN/BsWGazu9l9BEr1Q9OH22jBlvrKW83PDy1P40C/KYi87Pa1R8azKOFZB1Ui+e8nSa6JWqI2MMD32wie1H8nnm+n7EeMk4MSO7RwFord4LaKJXqo5eXZ7JxxsO8Zux3bmkR2u7w6k3XaOa0bFVU22n9wKa6JWqgxW7T/DXz7cxrlcbZo3qZnc49UpErG6WGSc4W1pudziqDjTRK3WRDuae5u631xMTEcL/+59E/Gye7LshjIpvTeHZMtL3aTdLT6aJXqmLUFxSxp1vrOVMaTlzp6W43axR9WVI1wgC/UXb6T2cJnqlaskYw+8/2sLmg3k8PTmJrlHN7Q6pwTQPCmBATDjfazu9R9NEr1QtvblqH4vWZnHPmDjG9mpjdzgNbmT3KLYfOcWRvGK7Q1EXyaVELyLjRWSHiGSIyEPVLO8hIitF5IyI3FdlWaaIbBaRDSKSXl+BK2WHNZk5/PnfPzKmR2t+NSbO7nAaxah4qyfRUm2+8Vg1JnoR8QdeAC4DegFTRKRXlWI5wD3AU+fZzCXGmCRjTEpdglXKTkfyipn55jo6hYfw98lJXnnytTrd2zSnbYtglnDMYFcAAB1pSURBVOw8Znco6iK5UqMfCGQYY/YYY84CC4FJzgWMMceMMWuAkgaIUSnbnSktY+Zbayk6W8rLU/sT1tQ7T75WR0QYFR/Fsl0nKC3TbpaeyJVE3wE44PQ4y/GcqwzwtYisFZEZ5yskIjNEJF1E0o8f10NE5V4e+eRH1u/P5f9dl+gxs0XVp5HdozhVXMr6A7l2h6IugiuJvrrjU1OL10g1xiRjNf3cJSIjqitkjJlrjEkxxqRERUXVYvNKNawFq/ezYPV+Zo7qymV929kdji1S4yLx9xPtfeOhXEn0WUAnp8cdgUOuvoAx5pDj7zHgQ6ymIKU8wrr9J/nTx1sZHhfJfePi7Q7HNi2CA+nfuZW203soVxL9GiBORGJFpAlwPfCJKxsXkWYiElpxHxgHbLnYYJVqTMdOFTPzzbW0CQviuSn98PeRk6/nMzI+ii0H8zl+6ozdoahaqjHRG2NKgdnAV8A24F1jzFYRuVNE7gQQkbYikgXcC/xeRLJEpAXQBkgTkY3AauAzY8yXDfVmlKovZ0vLueutdeSdLuHlm1JoGdLE7pBsVzGapXaz9DwuDZptjPkc+LzKcy853T+C1aRTVT6QWJcAlbLD/372I2syT/LM9Un0at/C7nDcQq92LYhsHsT3O49zTf/qvu7KXemVsUpVsWhtFvNX7uP24bFMSqpNBzPv5udnjWa5dNdxyspr0x9D2U0TvVJONmfl8fCHmxnaNYIHx/ewOxy3MzI+ityiEjZlaTdLT6KJXimH7IIz3PFGOlHNrZOvAf769ahqeLdI/ASdjMTD6CdZKaC0rJzZb68nu/AsL0/tT0TzILtDckutmjUhsVNLHbbYw2iiVwp4/IvtrNyTzV+v6kufDmF2h+PWRnVvzcasXHIKz9odinKRJnrl8z7ecJBX0vZyy9AY7U3igpHxURgDy3Zprd5TaKJXPu3HQ/k8+P4mBsaE87sJPe0OxyMkdAgjvFkTbb7xIJrolc/KLTrLHW+m07JpE164MZlAPfnqEj8/YXhcJEt3Hqdcu1l6BP1kK5/19H92ciSvmBdvSiYqVE++1sao+ChOFJzlx8P5doeiXKCJXvmkvKIS3k3PYlJSB/p1bmV3OB5neJw1HMKSHTrImSfQRK980sI1+zldUsatqbF2h+KRIpsHkdAxTNvpPYQmeuVzSsvKmb8ikyFdInQcmzoY2T2KdftzyTutE8u5O030yud8seUIh/KK+cUwrc3XxcjuUZSVG5ZnnLA7FFUDTfTK58xL20tMRAije7S2OxSPltSpJS2CA7Sd3gNoolc+Zd3+k2w4kMv01Fj8fHwikboK8PdjeFwU3+88jjHazdKdaaJXPmVe2l5aBAdwrV4BWy9GxkdxNP8M24+csjsUdQGa6JXPOJh7mi+3HGHKwM40C3Jpzh1Vg4pZp7T3jXvTRK98xvwVmQBMGxpjaxzepE2LYHq2a6Ht9G5OE73yCYVnSlmwej/j+7SlQ8umdofjVUZ2jyI98yQFZ0rtDkWdhyZ65RMWrc3iVHGpdqlsAKPioyjVbpZuTRO98nrl5YZ/Ld9Lv84tSdbhDupd/+hWNA8K0HZ6N6aJXnm9b7cfIzO7SGvzDSTQ34/UbhF8v0O7WborTfTK672atpf2YcGM793W7lC81sjurTmYe5rdxwvsDkVVQxO98mpbD+Wxck82Nw+N0cm+G9DI+IrRLLX5xh3pJ195tVfTMglp4s/1AzrbHYpX69CyKXGtm2s7vZvSRK+81rFTxfx74yGu7d+RsJBAu8PxeqPio/hhTw5FZ7WbpbvRRK+81pur9lNSXs50HXO+UYzs3pqzZeWs2pNtdyiqCk30yisVl5Tx1qp9jOnRmtjIZnaH4xMGxLaiaaA/32s7vdvRRK+80scbDpJdeJZbtUtlowkK8Gdo1wiWaDu929FEr7yOMYZ5aXvp0TaUIV0i7A7Hp4yMj2JfdhGZJwrtDkU50USvvM7yjGx2Hi3gF8NiEdEx5xvTqO7WZC46yJl70USvvM68tD1ENg9iYlJ7u0PxOZ0jQoiNbKbdLN2MJnrlVTKOFbB4x3GmDo4mKMDf7nB80sjuUazck01xSZndoSgHTfTKq/xr+V6aBPhx42C9QMouI+OjKC4pZ/XeHLtDUQ6a6JXXyC06y/vrsrgyqT2RzYPsDsdnDekSQVCAnw6H4EZcSvQiMl5EdohIhog8VM3yHiKyUkTOiMh9tVlXqfry9ur9FJeUa5dKmwUH+jOoSwTf79QTsu6ixkQvIv7AC8BlQC9gioj0qlIsB7gHeOoi1lWqzkrKynl9xT6GdYukR9sWdofj80Z1j2L38UIO5BTZHYrCtRr9QCDDGLPHGHMWWAhMci5gjDlmjFkDlNR2XaXqw+ebD3Mkv5hbh8XYHYriv6NZau8b9+BKou8AHHB6nOV4zhUurysiM0QkXUTSjx/XD4dyXcUFUl2imlX241b26hLZjE7hTbWd3k24kuiru+LE1WlkXF7XGDPXGJNijEmJiopycfNKwdp9J9mUlcf01Fj8/PQCKXcgIozsHsWK3Sc4W1pudzg+z5VEnwV0cnrcETjk4vbrsq5SLpmXtpewpoFck+zqgaZqDKO6t6bobBnpmdrN0m6uJPo1QJyIxIpIE+B64BMXt1+XdZWq0YGcIr7aeoQpAzsT0iTA7nCUkyFdIwj0F22ndwM1JnpjTCkwG/gK2Aa8a4zZKiJ3isidACLSVkSygHuB34tIloi0ON+6DfVmlO+ZvyITPxFuHhptdyiqimZBAQyICddE7wZcqgIZYz4HPq/y3EtO949gNcu4tK5S9aHMGN5Zc4Cf921Hu7CmdoejqjEqPoq/fr6dw3mn9X9kI70yVnmsY/nFnDpTqhdIubGRjl5QS7VWbytN9MojGQxH8ovpH92KpE4t7Q5HnUf3Ns1pFxas3SxtpoleeaSTRSWcKS3nF1qbd2sV3SzTdp2gpEy7WdpFE73ySIfzTtPE349xvdrYHYqqwaj4KE6dKWX9/ly7Q/FZmuiVx9lyMI9TxaW0CwsmwF8/wu5uaLdIAvxEBzmzkX5LlMeZl7YXPxGiQnUoYk/QIjiQ5OhW2k5vI030yqMczS/m002HaB0aRICffnw9xcjuUWw9lM+xU8V2h+KT9JuiPMobK/dRWm5oGxZsdyiqFkY5RrNcuvOEzZH4Jk30ymMUl5Tx1g/7GNuzDcE6H6xH6dWuBVGhQXqVrE000SuP8cG6g5wsKtELpDxQRTfLZbuOU1bu6uC3qr5oolcewRjDq8v30rt9CwbFhtsdjroII7tHkVtUwsYs7WbZ2DTRK4+wdNcJMo4V8IthsYjomPOeaHhcJH6C9r6xgSZ65RHmpe0lKjSIyxPa2x2KukgtQ5qQ1KmlttPbQBO9cnu7jp5i6c7jTBscTZMA/ch6spHdW7MpK5fsgjN2h+JT9Fuj3N6ryzMJCvDjxsE65rynGxUfhTGQlqHdLBuTJnrl1nIKz/LBuiyuTu5AeLMmdoej6qhvhzDCmzXh1bS9nCw8a3c4PkMTvXJrb/+wjzOl5dyaql0qvYGfn/CXSX3YdvgUV7+4gj3HC+wOySdooldu60heMa8uz2R4XCRxbULtDkfVkwkJ7VgwYxD5p0u4as4KVuzWZpyGpoleuaWSsnLuXrCO4pIy/nh5L7vDUfWsf3Q4H92VSuvQIKbNW83C1fvtDsmraaJXbumpr3awJvMkf7u6r9bmvVSn8BDenzWUod0ieeiDzfz182161WwD0USv3M5XW4/w8tI9TB0czaSkDnaHoxpQi+BAXr05hWlDopm7dA93vrmWwjOldofldTTRK7eyL7uQ+97bSELHMH5/eU+7w1GNIMDfj0cn9eGRK3rx7bajXPfSSg7nnbY7LK+iiV65jeKSMma+uQ4/EV64IZkgHaHSp9ySGsu8WwawP6eISc8vZ5OOiVNvNNErt/Hnf2/lx8P5PD05kU7hIXaHo2xwSXxr3p85lEB/P/7n5ZV8sfmw3SF5BU30yi0sWpvFgtUHmDWqK6N76ITfviy+bSgf3ZVKz3YtmPnWOl5YnIExepK2LjTRK9ttP5LP7z/azOAu4dw7trvd4Sg3EBUaxILbBzMxsT3/99UO7l+0ibOl5XaH5bEC7A5A+bZTxSXMfHMdocGBPDulHwH+WvdQluBAf565PokuUc34xze72J9TxEs39dehMC6CfquUbYwxPPT+ZvbnFPH8lH60DtV5YNVPiQi/+ll3nrk+iQ0HcrlqznIyjumwCbWliV7ZZv6KTD7bfJj7L41nUJcIu8NRbmxSUgcW3D6YwjOlXD1nOct19Mta0USvbLFu/0n+9/Nt/KxnG2YM72J3OMoD9I9uxYezUmkX1pSbX13NAh02wWWa6FWjyyk8y+y31tE2LJj/d10ifn46NaByTafwEBbNHEJqt0h++8FmHvv0Rx02wQWa6FWjKi83/OqdDZwoPMuLN/YnLCTQ7pCUhwkNDmTezSncMjSGV9L2cscb6TpsQg000atG9fziDJbuPM6fruhFnw5hdoejPFSAvx+PTOzNo5N6s3jHca59aSWHcnXYhPPRRK8aTdquEzz9zU6u6teBGwZ2tjsc5QWmDYnh1VsGkJVTxKQXlrPxgA6bUB1N9KpRHM47zT0L1xPXujn/e1UfRLRdXtWPkd2jeH/WUIICrGETPtukwyZU5VKiF5HxIrJDRDJE5KFqlouIPOtYvklEkp2WZYrIZhHZICLp9Rm88gwlZeXMfns9xSVlzLmxPyFN9Do9Vb+6t7GGTejTIYzZC9bx7poDdofkVmpM9CLiD7wAXAb0AqaISNUpfy4D4hy3GcCLVZZfYoxJMsak1D1k5Wme+GI7a/ed5PFrEujWurnd4SgvFdk8iLduG8TwuCge/GCTJnsnrtToBwIZxpg9xpizwEJgUpUyk4DXjWUV0FJE2tVzrMoDfbnlMK+k7eXmIdFMTGxvdzjKywUH+jN3an9N9lW4kug7AM57K8vxnKtlDPC1iKwVkRkXG6jyPJknCrn/vU0kdmrJwxN0EhHVOM5J9uma7F1J9NWdNat6hcKFyqQaY5KxmnfuEpER1b6IyAwRSReR9OPHj7sQlnJnxSVlzHxrHf7+wgs39NNJRFSjqkj2w7pF8uD7muxdSfRZQCenxx2BQ66WMcZU/D0GfIjVFHQOY8xcY0yKMSYlKirKteiV2/rTx1vZdjifpycn0bGVTiKiGl9woD//nJaiyR7XEv0aIE5EYkWkCXA98EmVMp8A0xy9bwYDecaYwyLSTERCAUSkGTAO2FKP8Ss39G76Ad5JP8Ddo7txSXxru8NRPkyTvaXGfm7GmFIRmQ18BfgDrxpjtorInY7lLwGfAz8HMoAiYLpj9TbAh44+0wHA28aYL+v9XSi38eOhfP7w0RaGdo3gVz/TSUSU/SqS/e2vp/Pg+5sQ4LqUTjWu501c6tBsjPkcK5k7P/eS030D3FXNenuAxDrGqDxEfnEJs95aS1jTQJ65vh/+OliZchPOyf6B9zcBvpXs9cpYVS+MMTy4aBMHTp7m+RuSiQoNsjskpX7CuRnngfc38Z4PNeNoolf14tXlmXyx5QgPjo9nYGy43eEoVS1fTfaa6FWdrd13kr99vo1xvdpwu04iotxc1WS/aG2W3SE1OE30qk6O5hcz++11tG/ZlP+7LlEHK1MewTnZ379oo9cne0306qJtOZjHpOeXk3e6hDk3JhPWVCcRUZ6jItmndvX+ZK+JXl2UL7cc5tqXVuAnsOjOoTqJiPJIwYH+vHKz9yd7TfSqVowxvLA4gzvfXEfPdi34aHYqvdq3sDsspS6aLyR7TfTKZcUlZdz77kb+76sdTEpqz4LbB9M6NNjusJSqs6rJ/n0vS/aa6JVLThSc4cZXfuDD9Qf5zdju/GNyEsGBOlCZ8h7Obfb3eVmy10SvarT9SD6Tnl/O1kN5zLkxmbvHxGnvGuWVmjbxzmSviV5d0LfbjnLNnBWUlpfz7h1D+HlfnU9GeTdvTPaa6FW1jDG8smwPt72eTmxUMz6+axgJHVvaHZZSjaIi2Q/tGsF9izbywTrPTvaa6NU5zpaW89D7m3nss22M792W9+4YStswPemqfEvTJv68Mm0AQ7tG8Jv3PDvZa6JXP3Gy8CxT5/1QOZ78Czck07SJnnRVvslbkr0melUp41gBV85ZzvoDufxjchK/GRePnw41rHxc1WQ/f0Um1sjsnkMTvQJg6c7jXDVnOYVnSllw+2Cu7Fd1/nelfFdFsh8d35o/fbKVexZuoOBMqd1huUwTveL1lZlMf20NHVo25aO7Uukf3crukJRyOxUnaO+/NJ7PNh1i4vNp7Dx6yu6wXKKJ3oeVlpXzh4+28MePt3JJfBSLZg7VibyVugA/P+GuS7rx5m2DyD9dyqTnl3tEu70meh+Vd7qE6a+t4Y1V+7hjRBdenppC8yCXZpZUyucN7RrJ5/cMo2/HMO59dyO//WAzxSVldod1XprofVDmiUKumrOcVXuyefKaBH778546v6tStdS6RTBv3zaIO0d2ZcHq/Vzz4gr2ZRfaHVa1NNH7mJW7s7lyznJOFp7lzV8M4n8G+M4EyUrVtwB/Px66rAevTEvhQE4Rlz+Xxldbj9gd1jk00fuQhav3M3XeD0Q2D+Kju1IZ1CXC7pCU8go/69WGz+4ZTkxEM+54Yy3/+9mPlJSV2x1WJU30PqCs3PDYpz/y0AebGdotkg9mDSU6opndYSnlVTqFh7Bo5hCmDo7mn8v2MmXuKo7kFdsdFgB69s1LFZeUsXpvDmkZJ1i8/Ri7jhVwy9AYfj+hJwH++vuuVEMICvDnL1f2ISWmFb/9YDMTnl3GM9f3Y1hcpK1xaaL3EuXlhh8P57Ns1wnSMo6zJvMkZ0vLaeLvR//oVswc1ZWrkzvaHaZSPmFSUgd6t2/BzDfXMfXVH/jVmO7cPbqbbVeaa6L3YFkni1iecYJlu06wYnc2OYVnAejRNpRpg6MZFhfJwNhwQprov1mpxtatdSgfz07l4Q828/Q3O1m7/yT/mJxEeLMmjR6LZgAPkl9cwsrd2aTtOkFaxgn2nrC6crUODWJUfBTD4yJJ7Rap0/sp5SZCmgTw9OQkBsSG8+dPfmTCs8t4/obkRr/6XBO9GyspK2fDgVyrOWbXcTZm5VFWbghp4s/gLhFMHRzN8LhIurVurjM+KeWmRIQbB0WT2LElM99ay+SXV/Lbn/fk1tSYRvveaqJ3I8YYdh8vJG3XcdIyTrBqTw4FZ0rxE0jo2JJZo7oyrFsk/Tq3okmAnlBVypP06RDGp7OH85v3NvKXT38kPTOHJ65NoEVwYIO/tiZ6Gxlj2HuikPTMk6zOzGF5xgkOO7pjRUeEMCmpPcPjIhnSJZKwkIb/MCilGlZYSCD/nNafuUv38ORXO9j2XBpzbuxPr/YtGvR1NdE3orOl5Ww5lEd6Zg7pmSdZu+8k2Y4TqK1CAhnSNYK7u1lt7Z3CdXAxpbyRiHDHyK7069yK2W+v46o5y/nLpD4NepW6JvoGlHe6hHX7TpK+L4c1mSfZeCCXM6XW1XIxESFc0qM1KdGtSIkJp2tUM21nV8qHDIwN57N7hvPLhet54P1NrM7M4S+T+jTIjG6a6OuJMYaDuadJzzzJmswc1u47yY6jpzAGAvyE3h3CuGlwNANiWtE/Opyo0CC7Q1ZK2SwqNIg3fjGIZ77ZyXOLM9hyMI/3Zw6lWT2PJKuJ/iKVlRu2Hc63mmH2nSQ98yRH8q329dCgAPpFt2JC33akxIST1KmlzruqlKqWv59w77h4kqNbsXpvTr0nedBE75KycsOJgjNkHCsgPdNqilm37ySFZ63xp9uHBTMwNryyth7fNlSH/VVK1cqo+NaMim/dINv2+URfXFLGkbxiDucVczS/mCP5xRzJc9zyreeOnTpDWbk1GbAI9Gjbgmv6d6S/o329Q8umNr8LpZQ6P5cSvYiMB54B/IFXjDGPV1kujuU/B4qAW4wx61xZt6EYYzhZVMIRpwR+OK+Yo3lOyTy/mLzTJeesGxoUQNuwYNqGBdOtdSRtW1j3O4eHkNS5ZaP0e1VKqfpSY6IXEX/gBWAskAWsEZFPjDE/OhW7DIhz3AYBLwKDXFy3XpSXG371zobKBH4kv5izpT8dD1oEopoHWUk7IoRBXcJp0yKYti2CaRcWTJsw635DtJEppZRdXMloA4EMY8weABFZCEwCnJP1JOB1Y4wBVolISxFpB8S4sG698PMT9mUXEhzoT7/OLStr4W1b/DeBR4UGEahD9CqlfIwrib4DcMDpcRZWrb2mMh1cXBcAEZkBzADo3LmzC2Gd6+PZwy5qPeWB2va1O4IG0yO8R41lgnrWXEapCq4k+uq6jxgXy7iyrvWkMXOBuQApKSnVllGq0mWNcqrHFg8OfLDGMm0ffrgRIlHewpVEnwU4X5vbETjkYpkmLqyrlFKqAbnSYL0GiBORWBFpAlwPfFKlzCfANLEMBvKMMYddXFcppVQDqrFGb4wpFZHZwFdYXSRfNcZsFZE7HctfAj7H6lqZgdW9cvqF1m2Qd6KUUqpaYnWUcS8pKSkmPT3d7jCUUspjiMhaY0xKdcu0r6FSSnk5TfRKKeXlNNErpZSX00SvlFJezi1PxorIcWBfA75EJHCiAbdfHzTGunP3+EBjrC8aI0QbY6KqW+CWib6hiUj6+c5OuwuNse7cPT7QGOuLxnhh2nSjlFJeThO9Ukp5OV9N9HPtDsAFGmPduXt8oDHWF43xAnyyjV4ppXyJr9bolVLKZ2iiV0opL+cziV5EOonIYhHZJiJbReSXdsdUlYgEi8hqEdnoiPHPdsd0PiLiLyLrReRTu2OpjohkishmEdkgIm45Qp5jys1FIrLd8bkcYndMzkQk3rH/Km75IvIru+OqSkR+7fi+bBGRBSISbHdMVYnILx3xbbVjH/pMG71jDtt2xph1IhIKrAWubIiJyi+WiAjQzBhTICKBQBrwS2PMKptDO4eI3AukAC2MMZfbHU9VIpIJpBhj3PYiGhGZDywzxrzimK8hxBiTa3dc1RERf+AgMMgY05AXM9aKiHTA+p70MsacFpF3gc+NMa/ZG9l/iUgfYCHW/NtngS+BmcaYXY0Vg8/U6I0xh40x6xz3TwHbsOa0dRvGUuB4GOi4ud0vsYh0BCYAr9gdi6cSkRbACGAegDHmrLsmeYcxwG53SvJOAoCmIhIAhOB+s9j1BFYZY4qMMaXA98BVjRmAzyR6ZyISA/QDfrA3knM5mkQ2AMeA/xhj3C5G4B/AA0C53YFcgAG+FpG1jonn3U0X4DjwL0cT2Csi0szuoC7gemCB3UFUZYw5CDwF7AcOY81u97W9UZ1jCzBCRCJEJARrkqZONaxTr3wu0YtIc+B94FfGmHy746nKGFNmjEnCml93oOOwz22IyOXAMWPMWrtjqUGqMSYZuAy4S0RG2B1QFQFAMvCiMaYfUAg8ZG9I1XM0K00E3rM7lqpEpBUwCYgF2gPNROQme6P6KWPMNuAJ4D9YzTYbgdLGjMGnEr2j3ft94C1jzAd2x3MhjsP4JcB4m0OpKhWY6GgDXwiMFpE37Q3pXMaYQ46/x4APsdpH3UkWkOV0xLYIK/G7o8uAdcaYo3YHUo2fAXuNMceNMSXAB8BQm2M6hzFmnjEm2RgzAsgBGq19Hnwo0TtOdM4Dthlj/m53PNURkSgRaem43xTrQ7zd3qh+yhjzW2NMR2NMDNbh/HfGGLeqQYlIM8cJdxzNIeOwDp/dhjHmCHBAROIdT40B3KZjQBVTcMNmG4f9wGARCXF8x8dgnX9zKyLS2vG3M3A1jbw/a5wc3IukAlOBzY42cICHjTGf2xhTVe2A+Y4eDn7Au8YYt+y+6ObaAB9a33sCgLeNMV/aG1K17gbecjSN7AGm2xzPORxtymOBO+yOpTrGmB9EZBGwDqs5ZD3uORzC+yISAZQAdxljTjbmi/tM90qllPJVPtN0o5RSvkoTvVJKeTlN9Eop5eU00SullJfTRK+UUl5OE71SSnk5TfRKKeXl/j/M1KNw06N07wAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dt = X_test\n", + "y_dt = y_test\n", + "\n", + "for _ in range(10):\n", + " i = randint(0, len(dt))\n", + "\n", + " plt.figure()\n", + " plt.title('{originalTitle} ({startYear:.00f}, {genres})'.format(**dt[i]))\n", + " preds = np.asarray([m.predict_proba([dt[i]])[0,1] for m in models])\n", + " preds = preds[1:] - preds[:-1]\n", + "\n", + " plt.plot(mids, preds, '-o', label='predicted distribution')\n", + " plt.plot([y_dt.iloc[i], y_dt.iloc[i]], [0, preds.max()], label='true rating')\n", + " plt.plot([(preds * mids).sum(), (preds * mids).sum()], [0, preds.max()], label='average prediction')\n", + " plt.plot([mids[preds.argmax()], mids[preds.argmax()]], [0, preds.max()], label='prediction mode')\n", + " plt.legend(loc='best')\n", + " plt.xlabel('Rating value')\n", + " plt.ylabel('Probabilty')\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Para pensar\n", + "\n", + "Pensando en el contexto de querer brindar una herramienta que guie la construccion de guiones\n", + "\n", + "* Nos da algún tipo de ventaja para construir un mejor producto esta técnica?\n", + "* Qué metrica offline podemos usar para evaluar este modelo?\n", + "* Habría una ventaja de utilizar un modelo basado en arboles?\n", + "* Para leer: Cómo se podría empaquetar este código en algo compatible con sklearn.linear_models.LogisticRegression? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/clase-3/02-hipotesis-elenco.ipynb b/notebooks/clase-3/02-hipotesis-elenco.ipynb new file mode 100644 index 0000000..bae91ff --- /dev/null +++ b/notebooks/clase-3/02-hipotesis-elenco.ipynb @@ -0,0 +1,1221 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Boilerplate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from google.colab import drive\n", + "\n", + "drive.mount('/content/gdrive')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Si hay cambios en el repositorio, con esta linea actualizas tu drive\n", + "!cd /content/gdrive/My\\ Drive/ml-practico/code; git pull" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('/content/gdrive/My Drive/ml-practico/code')" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "# Para trabajar local\n", + "sys.path.append('/Users/przivic/prog/machine_learning_practico')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/przivic/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3263: DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " if (await self.run_code(code, result, async_=asy)):\n" + ] + } + ], + "source": [ + "from lib import data\n", + "from pathlib import Path\n", + "import pandas as pd\n", + "\n", + "PATH = Path('../../data/')\n", + "ratings_df = data.load_title_ratings(PATH)\n", + "basics_df = data.load_title_basics(PATH)\n", + "principals_df = pd.read_csv(PATH / 'title.principals.tsv', sep='\\t')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Recordemos como era la data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tconstorderingnconstcategoryjobcharacters
0tt00000011nm1588970self\\N[\"Self\"]
1tt00000012nm0005690director\\N\\N
2tt00000013nm0374658cinematographerdirector of photography\\N
3tt00000021nm0721526director\\N\\N
4tt00000022nm1335271composer\\N\\N
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" + ], + "text/plain": [ + " tconst ordering nconst category job \\\n", + "0 tt0000001 1 nm1588970 self \\N \n", + "1 tt0000001 2 nm0005690 director \\N \n", + "2 tt0000001 3 nm0374658 cinematographer director of photography \n", + "3 tt0000002 1 nm0721526 director \\N \n", + "4 tt0000002 2 nm1335271 composer \\N \n", + "\n", + " characters \n", + "0 [\"Self\"] \n", + "1 \\N \n", + "2 \\N \n", + "3 \\N \n", + "4 \\N " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "principals_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "actor 9428011\n", + "actress 7087536\n", + "self 6992466\n", + "writer 5443248\n", + "director 4739306\n", + "producer 2533985\n", + "cinematographer 1472541\n", + "composer 1461940\n", + "editor 1375324\n", + "production_designer 302528\n", + "archive_footage 245691\n", + "archive_sound 2605\n", + "Name: category, dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "principals_df.category.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tconstorderingnconstcategoryjobcharacters
1025389tt012033810nm0365239editor\\N\\N
1025390tt01203381nm0000138actor\\N[\"Jack Dawson\"]
1025391tt01203382nm0000701actress\\N[\"Rose Dewitt Bukater\"]
1025392tt01203383nm0000708actor\\N[\"Cal Hockley\"]
1025393tt01203384nm0000870actress\\N[\"Molly Brown\"]
1025394tt01203385nm0000116director\\N\\N
1025395tt01203386nm0484457producerproducer\\N
1025396tt01203387nm0000035composer\\N\\N
1025397tt01203388nm0005665cinematographerdirector of photography\\N
1025398tt01203389nm0119322editor\\N\\N
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" + ], + "text/plain": [ + " tconst ordering nconst category \\\n", + "1025389 tt0120338 10 nm0365239 editor \n", + "1025390 tt0120338 1 nm0000138 actor \n", + "1025391 tt0120338 2 nm0000701 actress \n", + "1025392 tt0120338 3 nm0000708 actor \n", + "1025393 tt0120338 4 nm0000870 actress \n", + "1025394 tt0120338 5 nm0000116 director \n", + "1025395 tt0120338 6 nm0484457 producer \n", + "1025396 tt0120338 7 nm0000035 composer \n", + "1025397 tt0120338 8 nm0005665 cinematographer \n", + "1025398 tt0120338 9 nm0119322 editor \n", + "\n", + " job characters \n", + "1025389 \\N \\N \n", + "1025390 \\N [\"Jack Dawson\"] \n", + "1025391 \\N [\"Rose Dewitt Bukater\"] \n", + "1025392 \\N [\"Cal Hockley\"] \n", + "1025393 \\N [\"Molly Brown\"] \n", + "1025394 \\N \\N \n", + "1025395 producer \\N \n", + "1025396 \\N \\N \n", + "1025397 director of photography \\N \n", + "1025398 \\N \\N " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "principals_df[principals_df.tconst == 'tt0120338']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Distribución de cantidad de peliculas por nconst" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tconsttitleTypeprimaryTitleoriginalTitleisAdultstartYearendYearruntimeMinutesgenres
8tt0000009movieMiss JerryMiss Jerry01894.0\\N45.0[Romance]
144tt0000147movieThe Corbett-Fitzsimmons FightThe Corbett-Fitzsimmons Fight01897.0\\N20.0[Documentary, News, Sport]
498tt0000502movieBohemiosBohemios01905.0\\N100.0[no-genre]
570tt0000574movieThe Story of the Kelly GangThe Story of the Kelly Gang01906.0\\N70.0[Biography, Crime, Drama]
672tt0000679movieThe Fairylogue and Radio-PlaysThe Fairylogue and Radio-Plays01908.0\\N120.0[Adventure, Fantasy]
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" + ], + "text/plain": [ + " tconst titleType primaryTitle \\\n", + "8 tt0000009 movie Miss Jerry \n", + "144 tt0000147 movie The Corbett-Fitzsimmons Fight \n", + "498 tt0000502 movie Bohemios \n", + "570 tt0000574 movie The Story of the Kelly Gang \n", + "672 tt0000679 movie The Fairylogue and Radio-Plays \n", + "\n", + " originalTitle isAdult startYear endYear \\\n", + "8 Miss Jerry 0 1894.0 \\N \n", + "144 The Corbett-Fitzsimmons Fight 0 1897.0 \\N \n", + "498 Bohemios 0 1905.0 \\N \n", + "570 The Story of the Kelly Gang 0 1906.0 \\N \n", + "672 The Fairylogue and Radio-Plays 0 1908.0 \\N \n", + "\n", + " runtimeMinutes genres \n", + "8 45.0 [Romance] \n", + "144 20.0 [Documentary, News, Sport] \n", + "498 100.0 [no-genre] \n", + "570 70.0 [Biography, Crime, Drama] \n", + "672 120.0 [Adventure, Fantasy] " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basics_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Nos vamos a quedar solo con principals de las peliculas que nos interesan\n", + "# Posiblemente de cosas diferentes \n", + "movie_principals_df = principals_df[principals_df.tconst.isin(set(basics_df.tconst.unique()))]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.10394285472418875" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(movie_principals_df) / len(principals_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Distribucion de cantidad de peliculas por persona" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Number of films per person')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "(\n", + " movie_principals_df\n", + " .nconst\n", + " .value_counts()\n", + " .value_counts(normalize=True)\n", + " .sort_index()\n", + " .plot(logx=True, logy=True, label='movies')\n", + ")\n", + "(\n", + " principals_df\n", + " .nconst\n", + " .value_counts()\n", + " .value_counts(normalize=True)\n", + " .sort_index()\n", + " .plot(logx=True, logy=True, label='general')\n", + ")\n", + "\n", + "plt.legend(loc='best')\n", + "plt.title('Number of films per person')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Relación con ratings\n", + "\n", + "Experimento: vamos a ver ejemplos claros" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "movie_principals_df = movie_principals_df.merge(ratings_df, on='tconst')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tconstorderingnconstcategoryjobcharactersaverageRatingnumVotes
0tt00000091nm0063086actress\\N[\"Miss Geraldine Holbrook (Miss Jerry)\"]5.9154
1tt00000092nm0183823actor\\N[\"Mr. Hamilton\"]5.9154
2tt00000093nm1309758actor\\N[\"Chauncey Depew - the Director of the New Yor...5.9154
3tt00000094nm0085156director\\N\\N5.9154
4tt00001471nm0179163self\\N[\"Self\"]5.2356
\n", + "
" + ], + "text/plain": [ + " tconst ordering nconst category job \\\n", + "0 tt0000009 1 nm0063086 actress \\N \n", + "1 tt0000009 2 nm0183823 actor \\N \n", + "2 tt0000009 3 nm1309758 actor \\N \n", + "3 tt0000009 4 nm0085156 director \\N \n", + "4 tt0000147 1 nm0179163 self \\N \n", + "\n", + " characters averageRating numVotes \n", + "0 [\"Miss Geraldine Holbrook (Miss Jerry)\"] 5.9 154 \n", + "1 [\"Mr. Hamilton\"] 5.9 154 \n", + "2 [\"Chauncey Depew - the Director of the New Yor... 5.9 154 \n", + "3 \\N 5.9 154 \n", + "4 [\"Self\"] 5.2 356 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "movie_principals_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "person_metadata = (\n", + " movie_principals_df[movie_principals_df.numVotes > 1000]\n", + " .groupby('nconst')\n", + " .agg(\n", + " n_films=('tconst', 'count'), \n", + " avg_rating=('averageRating', 'mean'),\n", + " max_rating=('averageRating', 'max'),\n", + " min_rating=('averageRating', 'min'),\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "cohort = (person_metadata.n_films > 10) & (person_metadata.n_films < 30)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "person_metadata[cohort].avg_rating.hist(bins=30, alpha=0.5, label='avg_rating')\n", + "person_metadata[cohort].max_rating.hist(bins=30, alpha=0.5, label='max_rating')\n", + "person_metadata[cohort].min_rating.hist(bins=30, alpha=0.5, label='min_rating')\n", + "\n", + "plt.legend(loc='best')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parece haber señal, veamos ejemplos puntuales" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "person_metadata['link'] = (\n", + " person_metadata.index.to_series().apply(lambda nconst: f'link')\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
n_filmsavg_ratingmax_ratingmin_ratinglink
nconst
nm0531431127.7333338.37.1link
nm0604335117.0454557.46.7link
nm0741013117.3636368.26.6link
nm0040916157.8733338.37.4link
nm1190372147.7000008.57.1link
nm0001425127.6250008.26.7link
nm0085038117.7545458.27.0link
nm0792670117.2363647.96.6link
nm4043111207.8050008.67.1link
nm0003593197.3210537.96.8link
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import HTML\n", + "\n", + "df = person_metadata[cohort].sort_values('min_rating', ascending=False)[:100].sample(10)\n", + "HTML(\n", + " person_metadata[cohort]\n", + " .sort_values('min_rating', ascending=False)[:100]\n", + " .sample(10)\n", + " .to_html(escape=False)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Hay mucha gente de principios del siglo 20. Vamos a filtrar por peliculas mas actuales" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "movie_principals_df = movie_principals_df.merge(basics_df, on='tconst')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "person_metadata = (\n", + " movie_principals_df[(movie_principals_df.numVotes > 1000) & (movie_principals_df.startYear > 1970)]\n", + " .groupby('nconst')\n", + " .agg(\n", + " n_films=('tconst', 'count'), \n", + " avg_rating=('averageRating', 'mean'),\n", + " max_rating=('averageRating', 'max'),\n", + " min_rating=('averageRating', 'min'),\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "cohort = (person_metadata.n_films > 10) & (person_metadata.n_films < 30)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "person_metadata[cohort].avg_rating.hist(bins=30, alpha=0.5, label='avg_rating')\n", + "person_metadata[cohort].max_rating.hist(bins=30, alpha=0.5, label='max_rating')\n", + "person_metadata[cohort].min_rating.hist(bins=30, alpha=0.5, label='min_rating')\n", + "\n", + "plt.legend(loc='best')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "person_metadata['link'] = (\n", + " person_metadata.index.to_series().apply(lambda nconst: f'link')\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
n_filmsavg_ratingmax_ratingmin_ratinglink
nconst
nm0062362177.1117658.16.3link
nm0857621187.4277788.06.7link
nm0311508197.2210538.16.6link
nm0501921117.9818188.67.1link
nm0598971117.3727278.06.6link
nm0160883147.3142867.96.3link
nm3763301127.0500007.46.6link
nm0316795217.3904768.06.4link
nm2953454117.9090918.66.9link
nm0858128138.3000009.07.5link
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = person_metadata[cohort].sort_values('min_rating', ascending=False)[:100].sample(10)\n", + "HTML(\n", + " person_metadata[cohort]\n", + " .sort_values('min_rating', ascending=False)[:100]\n", + " .sample(10)\n", + " .to_html(escape=False)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/clase-3/03-stars-features.ipynb b/notebooks/clase-3/03-stars-features.ipynb new file mode 100644 index 0000000..ef8cfbb --- /dev/null +++ b/notebooks/clase-3/03-stars-features.ipynb @@ -0,0 +1,1194 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.pipeline import make_pipeline, make_union\n", + "from sklearn.feature_extraction import DictVectorizer\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics import roc_auc_score\n", + "from pathlib import Path\n", + "\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('/Users/przivic/prog/machine_learning_practico')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from lib import data, transformers\n", + "from lib.model import get_features_pipe, get_model_pipe" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading title basics...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/przivic/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3343: DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading title ratings...\n", + "Loading movie directors...\n", + "Merging everything...\n" + ] + } + ], + "source": [ + "PATH = Path('../../data/')\n", + "movies_df = data.load_data(PATH)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "principals_df = pd.read_csv(PATH / 'title.principals.tsv', sep='\\t')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tconstorderingnconstcategoryjobcharacters
0tt00000011nm1588970self\\N[\"Self\"]
1tt00000012nm0005690director\\N\\N
2tt00000013nm0374658cinematographerdirector of photography\\N
3tt00000021nm0721526director\\N\\N
4tt00000022nm1335271composer\\N\\N
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" + ], + "text/plain": [ + " tconst ordering nconst category job \\\n", + "0 tt0000001 1 nm1588970 self \\N \n", + "1 tt0000001 2 nm0005690 director \\N \n", + "2 tt0000001 3 nm0374658 cinematographer director of photography \n", + "3 tt0000002 1 nm0721526 director \\N \n", + "4 tt0000002 2 nm1335271 composer \\N \n", + "\n", + " characters \n", + "0 [\"Self\"] \n", + "1 \\N \n", + "2 \\N \n", + "3 \\N \n", + "4 \\N " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "principals_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "actor 9428011\n", + "actress 7087536\n", + "self 6992466\n", + "writer 5443248\n", + "director 4739306\n", + "producer 2533985\n", + "cinematographer 1472541\n", + "composer 1461940\n", + "editor 1375324\n", + "production_designer 302528\n", + "archive_footage 245691\n", + "archive_sound 2605\n", + "Name: category, dtype: int64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "principals_df.category.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Sacado del codigo de directores\n", + "\n", + "movies_stars = principals_df[principals_df.category.isin(['actress', 'actor'])].copy()\n", + "\n", + "# Calculo un ranking por pelicula segun el ordering\n", + "movies_stars['star_rank'] = (\n", + " movies_stars.sort_values('ordering')\n", + " .groupby('tconst')\n", + " .cumcount()\n", + ")\n", + "\n", + "first_star = movies_stars[movies_stars.star_rank == 0][['nconst', 'tconst']].rename(columns={'nconst': '1st_star'})\n", + "second_star = movies_stars[movies_stars.star_rank == 1][['nconst', 'tconst']].rename(columns={'nconst': '2nd_star'})\n", + "third_star = movies_stars[movies_stars.star_rank == 2][['nconst', 'tconst']].rename(columns={'nconst': '3rd_star'})" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "stars_df = (\n", + " first_star.merge(second_star, how='left', on='tconst')\n", + " .merge(third_star, how='left', on='tconst')\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1st_startconst2nd_star3rd_star
0nm0443482tt0000005nm0653042NaN
1nm0179163tt0000007nm0183947NaN
2nm0653028tt0000008NaNNaN
3nm0063086tt0000009nm0183823nm1309758
4nm3692297tt0000011NaNNaN
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1st_startconst2nd_star3rd_star
107363nm0000138tt0120338nm0000701nm0000708
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" + ], + "text/plain": [ + " 1st_star tconst 2nd_star 3rd_star\n", + "107363 nm0000138 tt0120338 nm0000701 nm0000708" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stars_df[stars_df.tconst == 'tt0120338']" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "movies_df = movies_df.merge(stars_df, on='tconst', how='left')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tconsttitleTypeprimaryTitleoriginalTitleisAdultstartYearendYearruntimeMinutesgenresaverageRatingnumVotesdirector1st_star2nd_star3rd_star
0tt0000009movieMiss JerryMiss Jerry01894.0\\N45.0[Romance]5.9154nm0085156nm0063086nm0183823nm1309758
1tt0000147movieThe Corbett-Fitzsimmons FightThe Corbett-Fitzsimmons Fight01897.0\\N20.0[Documentary, News, Sport]5.2356nm0714557NaNNaNNaN
2tt0000502movieBohemiosBohemios01905.0\\N100.0[no-genre]3.86nm0063413nm0215752nm0252720NaN
3tt0000574movieThe Story of the Kelly GangThe Story of the Kelly Gang01906.0\\N70.0[Biography, Crime, Drama]6.1589nm0846879nm0846887nm0846894nm3002376
4tt0000679movieThe Fairylogue and Radio-PlaysThe Fairylogue and Radio-Plays01908.0\\N120.0[Adventure, Fantasy]5.237nm0091767nm0000875nm0122665nm0933446
\n", + "
" + ], + "text/plain": [ + " tconst titleType primaryTitle \\\n", + "0 tt0000009 movie Miss Jerry \n", + "1 tt0000147 movie The Corbett-Fitzsimmons Fight \n", + "2 tt0000502 movie Bohemios \n", + "3 tt0000574 movie The Story of the Kelly Gang \n", + "4 tt0000679 movie The Fairylogue and Radio-Plays \n", + "\n", + " originalTitle isAdult startYear endYear runtimeMinutes \\\n", + "0 Miss Jerry 0 1894.0 \\N 45.0 \n", + "1 The Corbett-Fitzsimmons Fight 0 1897.0 \\N 20.0 \n", + "2 Bohemios 0 1905.0 \\N 100.0 \n", + "3 The Story of the Kelly Gang 0 1906.0 \\N 70.0 \n", + "4 The Fairylogue and Radio-Plays 0 1908.0 \\N 120.0 \n", + "\n", + " genres averageRating numVotes director 1st_star \\\n", + "0 [Romance] 5.9 154 nm0085156 nm0063086 \n", + "1 [Documentary, News, Sport] 5.2 356 nm0714557 NaN \n", + "2 [no-genre] 3.8 6 nm0063413 nm0215752 \n", + "3 [Biography, Crime, Drama] 6.1 589 nm0846879 nm0846887 \n", + "4 [Adventure, Fantasy] 5.2 37 nm0091767 nm0000875 \n", + "\n", + " 2nd_star 3rd_star \n", + "0 nm0183823 nm1309758 \n", + "1 NaN NaN \n", + "2 nm0252720 NaN \n", + "3 nm0846894 nm3002376 \n", + "4 nm0122665 nm0933446 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "movies_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ahora vamos a experimentar!\n", + "\n", + "Como podemos hacer para usar 1st_star y 2nd_star con el código que **ya** tenemos? [Miremos el diff](https://github.com/elsonidoq/machine_learning_practico/commit/1244da3daee2f7aff140d202885e6e8dba55c099)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "rating_data = data.load_rating_train_dev_test(movies_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "pipe = make_pipeline(\n", + " transformers.CrewFeatures('1st_star', min_cnt_movies=3),\n", + " DictVectorizer(sparse=False),\n", + " StandardScaler(),\n", + " LogisticRegression()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.6694917991958195, 0.5516081278474014)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe.fit(rating_data['X_train'], rating_data['y_train'] > 7.5)\n", + "\n", + "tr_auc = roc_auc_score(rating_data['y_train'] > 7.5, pipe.predict_proba(rating_data['X_train'])[:, 1])\n", + "dev_auc = roc_auc_score(rating_data['y_dev'] > 7.5, pipe.predict_proba(rating_data['X_dev'])[:, 1])\n", + "\n", + "tr_auc, dev_auc" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "pipe = make_pipeline(\n", + " make_union(\n", + " make_pipeline(transformers.CrewFeatures('1st_star', min_cnt_movies=3), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.CrewFeatures('2nd_star', min_cnt_movies=3), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.CrewFeatures('3rd_star', min_cnt_movies=3), DictVectorizer(sparse=False)),\n", + " ),\n", + " StandardScaler(),\n", + " LogisticRegression()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.7266249341879818, 0.5782582569516197)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe.fit(rating_data['X_train'], rating_data['y_train'] > 7.5)\n", + "\n", + "tr_auc = roc_auc_score(rating_data['y_train'] > 7.5, pipe.predict_proba(rating_data['X_train'])[:, 1])\n", + "dev_auc = roc_auc_score(rating_data['y_dev'] > 7.5, pipe.predict_proba(rating_data['X_dev'])[:, 1])\n", + "\n", + "tr_auc, dev_auc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Probando todo junto" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "pipe = make_pipeline(\n", + " make_union(\n", + " make_pipeline(transformers.CrewFeatures('1st_star', min_cnt_movies=3), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.CrewFeatures('2nd_star', min_cnt_movies=3), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.CrewFeatures('3rd_star', min_cnt_movies=3), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.CrewFeatures('director', min_cnt_movies=3), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.YearsAgo(), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.GenreDummies(), DictVectorizer(sparse=False)),\n", + " ),\n", + " StandardScaler(),\n", + " LogisticRegression()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.8433724090930335, 0.7368580621136858)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe.fit(rating_data['X_train'], rating_data['y_train'] > 7.5)\n", + "\n", + "tr_auc = roc_auc_score(rating_data['y_train'] > 7.5, pipe.predict_proba(rating_data['X_train'])[:, 1])\n", + "dev_auc = roc_auc_score(rating_data['y_dev'] > 7.5, pipe.predict_proba(rating_data['X_dev'])[:, 1])\n", + "\n", + "tr_auc, dev_auc" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "pipe = make_pipeline(\n", + " make_union(\n", + " make_pipeline(transformers.CrewFeatures('director', min_cnt_movies=3), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.YearsAgo(), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.GenreDummies(), DictVectorizer(sparse=False)),\n", + " ),\n", + " StandardScaler(),\n", + " LogisticRegression()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.8189300131210699, 0.7315217582213612)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe.fit(rating_data['X_train'], rating_data['y_train'] > 7.5)\n", + "\n", + "tr_auc = roc_auc_score(rating_data['y_train'] > 7.5, pipe.predict_proba(rating_data['X_train'])[:, 1])\n", + "dev_auc = roc_auc_score(rating_data['y_dev'] > 7.5, pipe.predict_proba(rating_data['X_dev'])[:, 1])\n", + "\n", + "tr_auc, dev_auc" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "pipe = make_pipeline(\n", + " make_union(\n", + " make_pipeline(transformers.YearsAgo(), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.GenreDummies(), DictVectorizer(sparse=False)),\n", + " ),\n", + " StandardScaler(),\n", + " LogisticRegression()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.7419233952069703, 0.7288300431976487)" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe.fit(rating_data['X_train'], rating_data['y_train'] > 7.5)\n", + "\n", + "tr_auc = roc_auc_score(rating_data['y_train'] > 7.5, pipe.predict_proba(rating_data['X_train'])[:, 1])\n", + "dev_auc = roc_auc_score(rating_data['y_dev'] > 7.5, pipe.predict_proba(rating_data['X_dev'])[:, 1])\n", + "\n", + "tr_auc, dev_auc" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "\n", + "pipe = make_pipeline(\n", + " make_union(\n", + " make_pipeline(transformers.CrewFeatures('1st_star', min_cnt_movies=3), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.CrewFeatures('2nd_star', min_cnt_movies=3), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.CrewFeatures('3rd_star', min_cnt_movies=3), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.CrewFeatures('director', min_cnt_movies=3), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.YearsAgo(), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.GenreDummies(), DictVectorizer(sparse=False)),\n", + " ),\n", + " GradientBoostingClassifier(),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.9175962044769267, 0.7461476500702391)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe.fit(rating_data['X_train'], rating_data['y_train'] > 7.5)\n", + "\n", + "tr_auc = roc_auc_score(rating_data['y_train'] > 7.5, pipe.predict_proba(rating_data['X_train'])[:, 1])\n", + "dev_auc = roc_auc_score(rating_data['y_dev'] > 7.5, pipe.predict_proba(rating_data['X_dev'])[:, 1])\n", + "\n", + "tr_auc, dev_auc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Word2Vec features" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "class EpochSaver: pass\n", + "\n", + "from gensim.models import Word2Vec\n", + "\n", + "w2v = Word2Vec.load('../../data/w2v/epoch_10')" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(100,)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "default_vector = np.mean(w2v.wv.vectors, axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### TODO: revisar a quien se parece este default_vector\n", + "\n", + "Seria mejor un vector se 0s? alguna otra agregacion sobre los datos?" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "x_i = rating_data['X_train'][0]\n", + "fields = ['1st_star', '2nd_star', '3rd_star', 'director']\n", + "min_cnt_movies = 2\n", + "\n", + "vectors = []\n", + "for field in fields:\n", + " person_id = x_i[field]\n", + " if person_id not in w2v.wv or w2v.wv.vocab[person_id].count < min_cnt_movies: continue\n", + " \n", + " vectors.append(w2v.wv[person_id])\n", + "\n", + "if len(vectors) == 0:\n", + " result = default_vector\n", + "else:\n", + " result = np.mean(vectors, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn.base import BaseEstimator, TransformerMixin\n", + "\n", + "class W2VCrewFeatures(BaseEstimator, TransformerMixin):\n", + " def __init__(self, w2v, fields, min_cnt_movies=2):\n", + " self.fields = fields\n", + " self.min_cnt_movies = min_cnt_movies\n", + " self.w2v = w2v\n", + "\n", + " def fit(self, X, y):\n", + " self.default_vector_ = np.mean(w2v.wv.vectors, axis=0)\n", + " return self\n", + " \n", + " def _get_movie_vector(self, x_i):\n", + " vectors = []\n", + " for field in self.fields:\n", + " person_id = x_i[field]\n", + " if person_id not in self.w2v.wv or self.w2v.wv.vocab[person_id].count < self.min_cnt_movies: continue\n", + "\n", + " vectors.append(self.w2v.wv[person_id])\n", + "\n", + " if len(vectors) == 0:\n", + " return self.default_vector_\n", + " else:\n", + " return np.mean(vectors, axis=0)\n", + " \n", + " def transform(self, X):\n", + " return np.asarray([self._get_movie_vector(x_i) for x_i in X])" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "pipe = make_pipeline(\n", + " W2VCrewFeatures(w2v, ['1st_star', '2nd_star', '3rd_star']),\n", + " StandardScaler(),\n", + " LogisticRegression()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/przivic/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:760: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.7050786145493632, 0.6466820465336702)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe.fit(rating_data['X_train'], rating_data['y_train'] > 7.5)\n", + "\n", + "tr_auc = roc_auc_score(rating_data['y_train'] > 7.5, pipe.predict_proba(rating_data['X_train'])[:, 1])\n", + "dev_auc = roc_auc_score(rating_data['y_dev'] > 7.5, pipe.predict_proba(rating_data['X_dev'])[:, 1])\n", + "\n", + "tr_auc, dev_auc" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "pipe = make_pipeline(\n", + " W2VCrewFeatures(w2v, ['1st_star', '2nd_star', '3rd_star', 'director']),\n", + " StandardScaler(),\n", + " LogisticRegression()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/przivic/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:760: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.7259572017485352, 0.6663948172895163)" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe.fit(rating_data['X_train'], rating_data['y_train'] > 7.5)\n", + "\n", + "tr_auc = roc_auc_score(rating_data['y_train'] > 7.5, pipe.predict_proba(rating_data['X_train'])[:, 1])\n", + "dev_auc = roc_auc_score(rating_data['y_dev'] > 7.5, pipe.predict_proba(rating_data['X_dev'])[:, 1])\n", + "\n", + "tr_auc, dev_auc" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "pipe = make_pipeline(\n", + " make_union(\n", + " W2VCrewFeatures(w2v, ['1st_star', '2nd_star', '3rd_star']),\n", + " W2VCrewFeatures(w2v, ['director'])\n", + " ),\n", + " StandardScaler(),\n", + " LogisticRegression()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/przivic/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:760: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.733849182294092, 0.6649772550701968)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe.fit(rating_data['X_train'], rating_data['y_train'] > 7.5)\n", + "\n", + "tr_auc = roc_auc_score(rating_data['y_train'] > 7.5, pipe.predict_proba(rating_data['X_train'])[:, 1])\n", + "dev_auc = roc_auc_score(rating_data['y_dev'] > 7.5, pipe.predict_proba(rating_data['X_dev'])[:, 1])\n", + "\n", + "tr_auc, dev_auc" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "pipe = make_pipeline(\n", + " make_union(\n", + " W2VCrewFeatures(w2v, ['1st_star', '2nd_star', '3rd_star']),\n", + " make_pipeline(transformers.CrewFeatures('1st_star', min_cnt_movies=3), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.CrewFeatures('2nd_star', min_cnt_movies=3), DictVectorizer(sparse=False)),\n", + " make_pipeline(transformers.CrewFeatures('3rd_star', min_cnt_movies=3), DictVectorizer(sparse=False)),\n", + " ),\n", + " GradientBoostingClassifier()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipe.fit(rating_data['X_train'], rating_data['y_train'] > 7.5)\n", + "\n", + "tr_auc = roc_auc_score(rating_data['y_train'] > 7.5, pipe.predict_proba(rating_data['X_train'])[:, 1])\n", + "dev_auc = roc_auc_score(rating_data['y_dev'] > 7.5, pipe.predict_proba(rating_data['X_dev'])[:, 1])\n", + "\n", + "tr_auc, dev_auc" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/clase-3/04-crew-embeddings.ipynb b/notebooks/clase-3/04-crew-embeddings.ipynb new file mode 100644 index 0000000..23dfe7c --- /dev/null +++ b/notebooks/clase-3/04-crew-embeddings.ipynb @@ -0,0 +1,428 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting gensim\n", + " Downloading gensim-3.8.3-cp38-cp38-macosx_10_9_x86_64.whl (24.2 MB)\n", + "\u001b[K |████████████████████████████████| 24.2 MB 9.4 MB/s eta 0:00:01 |██████████████████████ | 16.7 MB 8.9 MB/s eta 0:00:01\n", + "\u001b[?25hRequirement already satisfied: scipy>=0.18.1 in /Users/przivic/anaconda3/lib/python3.8/site-packages (from gensim) (1.5.0)\n", + "Requirement already satisfied: numpy>=1.11.3 in /Users/przivic/anaconda3/lib/python3.8/site-packages (from gensim) (1.18.5)\n", + "Requirement already satisfied: six>=1.5.0 in /Users/przivic/anaconda3/lib/python3.8/site-packages (from gensim) (1.11.0)\n", + "Collecting smart-open>=1.8.1\n", + " Downloading smart_open-3.0.0.tar.gz (113 kB)\n", + "\u001b[K |████████████████████████████████| 113 kB 6.2 MB/s eta 0:00:01\n", + "\u001b[?25hRequirement already satisfied: requests in /Users/przivic/anaconda3/lib/python3.8/site-packages (from smart-open>=1.8.1->gensim) (2.19.1)\n", + "Requirement already satisfied: idna<2.8,>=2.5 in /Users/przivic/anaconda3/lib/python3.8/site-packages (from requests->smart-open>=1.8.1->gensim) (2.7)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /Users/przivic/anaconda3/lib/python3.8/site-packages (from requests->smart-open>=1.8.1->gensim) (2018.11.29)\n", + "Requirement already satisfied: urllib3<1.24,>=1.21.1 in /Users/przivic/anaconda3/lib/python3.8/site-packages (from requests->smart-open>=1.8.1->gensim) (1.23)\n", + "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /Users/przivic/anaconda3/lib/python3.8/site-packages (from requests->smart-open>=1.8.1->gensim) (3.0.4)\n", + "Building wheels for collected packages: smart-open\n", + " Building wheel for smart-open (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for smart-open: filename=smart_open-3.0.0-py3-none-any.whl size=107097 sha256=564a8f8126de510dc7cb44407b867cfd4ca62a11085c95255994d564825851d0\n", + " Stored in directory: /Users/przivic/Library/Caches/pip/wheels/11/73/9a/f91ac1f1816436b16423617c5be5db048697ff152a9c4346f2\n", + "Successfully built smart-open\n", + "Installing collected packages: smart-open, gensim\n", + "Successfully installed gensim-3.8.3 smart-open-3.0.0\n", + "\u001b[33mWARNING: You are using pip version 20.1; however, version 20.2.3 is available.\n", + "You should consider upgrading via the '/Users/przivic/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install gensim" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "PATH = Path('../../data/')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('/Users/przivic/prog/machine_learning_practico')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from lib import data, transformers\n", + "from lib.model import get_features_pipe, get_model_pipe" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "from tqdm.notebook import tqdm\n", + "import csv" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "from gensim.models.callbacks import CallbackAny2Vec\n", + "from itertools import groupby\n", + "\n", + "class IterSentences:\n", + " def __init__(self, data_path, some=False):\n", + " self.data_path = data_path\n", + " self.some = some\n", + " \n", + " def __iter__(self):\n", + " reader = csv.DictReader((self.data_path / 'title.principals.tsv').open(), delimiter='\\t')\n", + " for i, (tconst, rows) in enumerate(groupby(tqdm(reader), lambda x: x['tconst'])):\n", + " if self.some and i == 1000: break\n", + " yield [e['nconst'] for e in sorted(rows, key=lambda x: int(x['ordering']))]\n", + " \n", + "\n", + "class EpochSaver(CallbackAny2Vec):\n", + " def __init__(self, data_path, fname_prefix=''):\n", + " self.data_path = data_path\n", + " self.fname_prefix = fname_prefix\n", + " self.epoch = 1\n", + " \n", + " def on_epoch_end(self, model):\n", + " print(f'Finished epoch {self.epoch}. Saving...')\n", + " prefix = f'{self.fname_prefix}_' if self.fname_prefix else ''\n", + " \n", + " output_path = self.data_path / 'w2v' / f'{prefix}epoch_{self.epoch}'\n", + " output_path.parent.mkdir(parents=True, exist_ok=True)\n", + " \n", + " model.save(str(output_path))\n", + " self.epoch += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "from gensim.models import Word2Vec" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "model = Word2Vec(window=10, iter=10, callbacks=[EpochSaver(PATH)])" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1a5979e0d5b245a488b4f2156ee1f406", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "model.build_vocab(IterSentences(PATH))" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "40f21178d2444484bbccee8ca152c559", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Finished epoch 1. Saving...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "48ac47938826485e988a3cc0fec1eb8c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Finished epoch 2. Saving...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "74a26ca096874c208ee6a278f5ec21c7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Finished epoch 3. Saving...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c89cb8874ed64234b40fe1e5d19740dd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Finished epoch 4. Saving...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c4ab9691fffd4fc28fc042a92ad6498a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Finished epoch 5. Saving...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "47eb4c316a524cc8a7ab0f2e6342cf22", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Finished epoch 6. Saving...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3e46ff92ae674587ab24a31756eb50d8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Finished epoch 7. Saving...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cfb1efa6f61241729d0d0a393d9f710a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Finished epoch 8. Saving...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5f9e155b7978459181172bcc5c5281b4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Finished epoch 9. Saving...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ec3186bce1bb426ca00a63290ee41679", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Finished epoch 10. Saving...\n" + ] + }, + { + "data": { + "text/plain": [ + "(362470430, 410851810)" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.train(IterSentences(PATH), total_words=model.corpus_total_words, epochs=10)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/clase-3/05-crew-embeddings-check.ipynb b/notebooks/clase-3/05-crew-embeddings-check.ipynb new file mode 100644 index 0000000..8bf7010 --- /dev/null +++ b/notebooks/clase-3/05-crew-embeddings-check.ipynb @@ -0,0 +1,2218 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install beautifulsoup4" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from gensim.models import Word2Vec" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class EpochSaver: pass" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "DATA_HOME = '../../data/w2v'\n", + "DATA_HOME = '../../../machine_learning_practico/data/w2v/'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir -p $DATA_HOME\n", + "!wget https://machine-learning-practico.s3.amazonaws.com/w2v/epoch_10 -O $DATA_HOME/epoch_10\n", + "!wget https://machine-learning-practico.s3.amazonaws.com/w2v/epoch_10.trainables.syn1neg.npy -O $DATA_HOME/epoch_10.trainables.syn1neg.npy\n", + "!wget https://machine-learning-practico.s3.amazonaws.com/w2v/epoch_10.wv.vectors.npy -O $DATA_HOME/epoch_10.wv.vectors.npy" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "DATA_HOME = Path(DATA_HOME)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "model = Word2Vec.load(str(DATA_HOME / 'epoch_10'))" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import HTML, display\n", + "from bs4 import BeautifulSoup\n", + "import requests\n", + "import requests_cache\n", + "\n", + "requests_cache.install_cache('imdb')\n", + "\n", + "def get_name(id):\n", + " response = requests.get(f'https://www.imdb.com/name/{id}/')\n", + " soup = BeautifulSoup(response.content)\n", + " return soup.select('.header .itemprop')[0].text\n", + "\n", + "def get_image(id):\n", + " response = requests.get(f'https://www.imdb.com/name/{id}/')\n", + " soup = BeautifulSoup(response.content)\n", + " candidates = soup.select('#name-poster')\n", + " return candidates[0].attrs['src'] if candidates else 'https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png'\n", + "\n", + "def render_person(id):\n", + " name = get_name(id)\n", + " picture = get_image(id)\n", + " return f\"\"\"\n", + "
\n", + "

{name}

\n", + "
{id}
\n", + " \n", + " \n", + " \n", + "
\n", + " \"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def show_similars(id, n=10):\n", + " display(HTML(render_person(id)))\n", + " renders = []\n", + " for similar_id, score in model.wv.most_similar(id, topn=n):\n", + " renders.append(render_person(similar_id))\n", + " \n", + " carousel = ''.join(\n", + " [\n", + " f'
{p}
' \n", + " for p in renders\n", + " ]\n", + " )\n", + " display(HTML(f'
{carousel}
'))" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from random import choice\n", + "\n", + "for _ in range(10):\n", + " id = choice(candidates)\n", + " show_similars(id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/practico-2/bias-variance-underfitting-overfitting.ipynb b/notebooks/practico-2/bias-variance-underfitting-overfitting.ipynb new file mode 100644 index 0000000..6c56297 --- /dev/null +++ b/notebooks/practico-2/bias-variance-underfitting-overfitting.ipynb @@ -0,0 +1,941 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Intuiciones respecto a la relacion entre bias / variance / underfitting y overfitting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generemos esto con un dataset sintético con una relacion cuadratica entre `x` e `y`" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def sample_data(size=100, x_matrix=False, min_x=-2.5, max_x=2.5):\n", + " x = (np.random.random(size=size) - 0.5 ) * (max_x - min_x)\n", + " x.sort() # util para graficar\n", + " y = x ** 2 + x + np.random.normal(0, 1, size=size)\n", + " if x_matrix:\n", + " x = x.reshape((-1,1))\n", + " return x, y" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "X, y = sample_data(size=100, x_matrix=True)\n", + "plt.scatter(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Underfitting\n", + "\n", + "Vamos a ajustar esta función cuadratica con una regresion lineal" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "lr = LinearRegression().fit(X, y)\n", + "\n", + "m, M = X.min(), X.max()\n", + "\n", + "plt.plot([m, M], lr.predict([[m], [M]]), '--k')\n", + "plt.scatter(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Este es un caso típico de underfitting, la capacidad del modelo no es suficiente para explicar los datos" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Como se comporta con una parte de la distribución que nunca vio?\n", + "\n", + "Vamos a evaluar la regresion lineal en un intervalo de datos que nunca vio, que involucran al `[-5, 5]` en lugar de `[-2.5, 2.5]` con lo que fue entrenado" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "oos_X, oos_y = sample_data(min_x=-5, max_x=5, x_matrix=True)\n", + "\n", + "m, M = oos_X.min(), oos_X.max()\n", + "plt.plot([m, M], lr.predict([[m], [M]]), '--k')\n", + "\n", + "plt.scatter(oos_X, oos_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La prediccion esta determinada por el sesgo que introdujimos nosotros. Este sesgo viene al determinar que el espacio de modelos que vamos a explorar, corresponde solo al de las funciones lineales en x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fiteamos muchos modelos lineales\n", + "\n", + "Veamos como varian las predicciones si entrenamos muchos modelos lineales.\n", + "\n", + "Podemos ver que el error es sistemáticamente alto" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "def sample_predictions(model, x, iters=100):\n", + " \"\"\"\n", + " Toma `iters` muestras de datos, entrena un modelo con esos datos \n", + " Devuelve las predicciones de los modelos\n", + " \"\"\"\n", + " ys_hat = []\n", + " for _ in range(iters):\n", + " X_train, y_train = sample_data(100, x_matrix=True)\n", + " model.fit(X_train, y_train)\n", + " ys_hat.append(model.predict(x))\n", + " return np.asarray(ys_hat)" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [], + "source": [ + "def expected_prediction(model, x, iters=100):\n", + " return sample_predictions(model, x, iters=iters).mean(axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ys = sample_predictions(LinearRegression(), X)\n", + "plt.plot(X, ys.T, color='k', alpha=0.05);\n", + "plt.scatter(X, y)\n", + "plt.plot(X, expected_prediction(LinearRegression(), X), lw=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Podemos ver que si bien con distintos samples de los datos se obtienen diferentes modelos, son todos bastante parecidos entre si, y hacen predicciones similares (baja varianza)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cambiando regresion lineal por arbol de decisión" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2f5d3662750443689a43ec0e0d7e6666", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=1, description='min_samples_leaf', max=50, min=1), Output()), _dom_class…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeRegressor\n", + "from ipywidgets import interact, widgets\n", + "\n", + "def draw(min_samples_leaf):\n", + " dt = DecisionTreeRegressor(min_samples_leaf=min_samples_leaf).fit(X, y)\n", + "\n", + " plt.plot(X, dt.predict(X), '--k')\n", + " plt.scatter(X, y)\n", + " \n", + "interact(\n", + " draw, \n", + " min_samples_leaf=widgets.IntSlider(min=1, max=50, step=1, value=1)\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Que pasa si lo miramos con observaciones fuera del rango entrenado?" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeRegressor\n", + "\n", + "plt.plot(oos_X, dt.predict(oos_X), '--k')\n", + "plt.scatter(oos_X, oos_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Podemos ver como se ajusta a la perfeccion a los datos\n", + "\n", + "# Haciendo muchas predicciones" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ys = sample_predictions(DecisionTreeRegressor(), X)\n", + "plt.plot(X, ys.T, color='k', alpha=0.05);\n", + "plt.scatter(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Podemos ver que al tomar muchas muestras los modelos terminan cubriendo la zona de alta densidad de bolitas\n", + "\n", + "Podemos ver que el modelo esperado se parece mucho a la funcion que genera los datos" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ys = sample_predictions(DecisionTreeRegressor(), X)\n", + "plt.plot(X, ys.T, color='k', alpha=0.05);\n", + "plt.scatter(X, y)\n", + "plt.plot(X, expected_prediction(DecisionTreeRegressor(), X), lw=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Podemos ver que en esperanza el modelo le pega muy bien, sin embargo en cada realizacion del dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Como se ve con observaciones out of sample?" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ys = sample_predictions(DecisionTreeRegressor(), oos_X)\n", + "plt.plot(oos_X, ys.T, color='k', alpha=0.05);\n", + "plt.scatter(X, y)\n", + "plt.plot(oos_X, expected_prediction(DecisionTreeRegressor(), oos_X), lw=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Calculando sesgo y varianza" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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MjiSrid5zyUayl/NMyo2lbzbV6ugojqFOlrGtTlo59IL3cULn2ECOFWTUc3vttVdZuQvnyBCWSSinkIKYBSBpiJYZEtJFmMXWjRIy1wn7C17wguIFtJPqYhe7WFk25a7mch7qPOUjjpQN4SZoJ510UucsQj4EBdlE9LbYYovyW6cGZF/yDO+muBSeyctd7nLN4YcfXpbnKTzXuw7XLOE/6UlPKnEcFKOylyVpE6+xfrFcQGEg40j+AQccUJYBEPRou+/6QfyJAG3L4HbEkQsEe5xZ4bJgk/VMBBKByRCgJyYhlLVeZ0se+tCHFqfBMunQyRBa+d3InYm85e0dd9yxede73tWIGTzmmGOaI488sqwijiJRk5TOZlsG9hJ1IV08yBxL+njeiZ3huHjgAx9YPKPCs4bapk6cI371Zuutty4buhDFPjlyP87B44tM8kp6ubmNPYgiG6c89r0vJaHsQ2aM84SLJ8vyqOVp5MNSMrLmhaoEoC/pPB4uBESn2cGF6PFy+uR1HOo4+SqfgBx44IGFwMQmG+e7EkVFWMRXmtHxvPXd63n3ixV98IMfXNq1wQYbNMcdd9z5hFhbHJF8J3gE3sYghGxUW+LZRfhU/zahFMOCILYJpXa5xgtrcmBAiXUSV4mAJqFchB7NOiQCaw8B+mQSQkmn2OAhdEjo04tf/OJizHNi+r99CFe2hf3kjOEttCESwaOHHWIdZ4Wb8pAqXlAEjeeO/V8t26YdOIMwM3LBLscGnf9F5v/+U19149DBTYS7cT7Jo53ci2jiE/K3N4RDCEdASq3CcVbxBvu/LyWh7ENmjPO8ezaciKHkzeKGvtrVrlZc734Hk2B3peg8XkyxH7x+yIdNHWLwLHN0xTa28zJYzB7WrFlTllgFbw8F6xJIA9CyrR1gvvcNtqijOFEbTC596Us3m222WfFqhndOfdyHPMbg9b8EG7GT4kx47vrKabdpEf7XhppQ2qDURyj1gYBwRJNXWbsFQFNuFE9fSMEitDPrkAgkAvNHgD6ZhFDyEp1++uklhhsREBI1NPHXAmXUx/xbtXZL0FY2ha61Osjuip+kp513BB6zqmk4D3gIeUTtb0Awx7FtUZf6M+pVn/N9KJEDv2rDKWRVchzHFdvMFonxxzesHtbl+E7mkMnnPOc5ZbVUeJdNR2yf+3l92Th7JYR59aUklH3IjHEe0/fuKeRMfIKZpF2+NmRg+a7XHRdZEkDxEF7tY3Z1yimnFDIq7k78HTJqFtD1bOThE2FF2gQGI6I2xBCAPuUjPyRSnKW6qkNfGc4Tsre+9a2lTpe97GXLju0zzzzzvBlZ3MM7h1CHV9V5beepe9SjHlW8dH3l1O1ZlO/qilBy+1PofYRSPyKMNue4j7cZZp410eDd9f8ytX1R+iDrkQisKwgY/+MSSvfSKZwDbIkVHuE2oxKyY6Lv8L1P5zgfx6g8F/W6+tO97Jx4frGTVs84ZjhU5tU+zgK2zq5nq5A2yQxhHfhFfXkA2QP1Vv+oZ/RdXHO+L7nGhnNY2Czzspe9rJdnRB7qzbsofMKGHmFvNUdQvjzJHM+nTTycWviEw6bUe9zjHqXdsTEn8m5/JqFsIzLm/zrWcibiCHA78LiD7Qy27O0H1imRtnD4n+DwZgmWRUi9Bd+sR4yiOEykjfC1n21XDaG0m1uZYi95EM2aECCENAS0Ft4gspa+a09jO+8gS4jnFa5wheZKV7pScYHHDFDdCCLl5zVElv0t03hO4rnzY/LIsnqOaku7/LX5v7r2EUo76cXQaB98xbQg6GbJQSgNVoN4nD5cm+3MshOBRGD+CNAnkxBK+psHSoyeSTkd25foGh4jk3mrVSay3oFr1Sh0cTxLH6kH3W8FzP/LlrTXihdbQ/eazCOTbC4PGm+ujShss3tnaXfgzHGCdFnutilmKH/X2EheTP3Ds2jFDxHlDYyYRNcQVZs9kT3kjv3oy1vbbKhl7xE9E46+e/WvflammEhcAUeI/D1HVmyitYwftt6bSnh9HbHJdJdddilchd3rS0ko+5AZcd5gJbjIJDexGANEMJaHvWqH4Lc7moARAPEJdhGLwbBkatmal9Hz8m0rg67qIDUUiHdC2l3uMFvjFeQxNXuzrK5u7lW2QUaAfG/XrS7DdQPGLjmvMiJYBhOFRECRUQPCILGsTcDr9qq/MpHJcdqibPWJOqrnrI5Rba3bHfXoI5TaatAj5mJTwpMgIDwI5RCu7bKivEmf6cpnXufUbZHrN692Z76JwCwQMHbGJZR0lZUNHjAxb4gG49+V5MuA0/UcGH7EAmEQH/++972v6Pl63CIi9JV3J4q392x9vauMRTqnrmwOJ44VIe2IjaycMTxwzon/4+Sgw4fa51r76Guv+9hRuAlN8xq58Ib2PaMv2QmrfIifg4fQzmnfTz755OYDH/hAqa/2OI+s8g6KXfR8V+IssjLpXZveLjOKL8gHz7Aaauf70UcfXXCMtltNFFvpvcrq4J2n7QOulss5lPrqpa5JKLt6bMQ5HYFUGewExC4zgm7W5CXndnp71YOZZk2mfDfbPOGEE4oCEP/g/1e84hVlecNMywabmpgNVUXHim+wmy1e68NTaZZh2d2SCaKK7BFeAbxInnpow1DSPh5Gm0suetGLllkK8iwf8T1mU2Y6hN8mIqS6PZMOgR0qp76mbgaHcsVszOKQlzzlPW5S7y5C6fdOTRS8YNa7zniVN9100/KzVAYibIa8vu3ylRMEn5IYGqjtZ1frf7JiUmB2HrKzWmX3lRNy1ffZ99y057vKizzjWvy/Gp9RZt/natQhyxiNgP4Zh1C6L1avOAY4Cniv6OJ2ci+iaaXLJBdRpJORAq+f23333f/H/hjHJsB2J/M48bCNa2faZa/N/+lXjhseNQ4OcYE2siLRMPDuZBtHbc7p08WwgymdS887kCqOFs+4Blu62b0S/DhY7LhH9pXB5vcl99OZr3/960ufiEm0Esmryo4ipd7Koo+9x9gEwAvZeQb1vd3qffV3nqOIzWF/YkVzqC5sM7wQSs4skwt1lNgdGPCM4gh9B0INm8Ckq7wklF2ojDgHUMZVPIEBikj43yyAW9kLVXmsEExCKXmGskAig4RSMmYwhI13UmebKRD0cVLkiXjxJCKQYh0NMC9Zd6iL2EwDTjlRpyGhULYBxePmNzy90JySMmOmqMyuKC4DCwneaaedCgYG5EqT+hByLwy3hLznnnvO5JCXgdQmu0P1VJcuQilGRzA2nMWvUMyWWswSDe7wUA7lHdeUQTbM+OyG52lG3BYtUSCMmrhe5JwSHSU782gD5WeMwYic9R2uuw+2nplFXaOvlEkuyJLDd2WFcTJmZlHeEH5rE4ehei3TNRgyyvpOn8Xh/67Ddec949lJE5kYl1AaX8gKW2DZ22pWV5lIAIcFb5OlXj+1hwiwLWwAXW1p27PKZ3vE0bEDnA2eI8PzltdJsRp1f7TF2EOqhBqxnWIKtdd5GHb1lWfhhjwKC+A5ZG/tZUBQef3kifQJ1dJngT19YmUKvuwewjekr8mLTS3qJxxM3+gDz3Ao8SRz/OAJlultLjIx8Lo5YW9sgjZ09Y82IH/IKPvs5x+HdI88lIvckiueSs8HN4G5e8Y5RvVPEspRCHVcJ2SIoPhHg5kA6hyD//73v38Z0Lx2yCPBklxHHngL7UYjYITAICBIl7nMZcqsRYwFwRs3EQIDiEKxA0t9CDGlIe7R7mweRiST8LlHp8dA6SpHnpQNT5z3T3pWIDIl5Hlxla6pN4Ll9RaIKsM6TTKjExNqpmnX+ywOeRn8MBo3aT/F1N6Ug5CbVZpRClb2U1RwRiopcO8k03eeH5VCXrSRkhoV7Dwqv3ld16eUndmz/hanNaS85lEPeDICyvaLUsgtY+D9apb7nIOfw3fknFeaPMF5nP7oq7dxgkgap4wOmXBYdvNplcISpVguE7sY7335TXNeO+gd7dPWIRwYJHiJ1SLL0+IwTb0X6VkY0gXIgVAjxtiyo0//07/1wevnmk86lhwO6c6utipzXEJJZv18LmIhZq3vbR/GJfuCSFkhc584/iBY4vPreHdtfsYznlFWskyIydC4sqr+2jyrQ37TJM8jW5aFxZlydiBJbBaypZ5dZRgDvGwIpDeycFp4FZyxzC4LG7DiZuVpjz32KKRTfpJnEURL0xwIdD092JWUbTmcA0I++oVdUC+6RH+Jf+SQYFPIhv7zsnSbsITC8bBG2e0y5GNl0vuO2R/L++Syq82edV75yLP7991330Kcp7XX7Xr5PwllFyojzhEu7m8dj1jEoOdhQxB4BLniKX0DWYcSdjMEAz3iYgik75alEUreTRs+5D8qyTMEiIAZYMpQL5uDBHNbCudNpJzWW2+9QgzNes3O+oRVufI1gzEYxKYglfJCmJXhIDgMKKUXO7nVQ/JJWU26jKvdBoYBZuDP4jCw5TnU3jbW2t9FKCkf7/5iqHkHGCVKwxKFUIdxCSV8tFEf8e5SaBTEOP3eruu8/1dXRs7snTyYbfNYkrfVSvqDLPA0M6Bm9Q6/SrT//vsXY8JAILwmNw4TK8tfCFXfTH9U/ckMOUA4EGpjV9gD8mhiRRZ4wBki9TEZGvJajCpv1HU4kDuvADGZIXPKZeS0l1GFA0xg4B4hNDxefnWLAZGH5FPfOsfYkD3t9QmvOOce5+M+1/wf+Yyq86Jd1x56TD/Ch/4yWSbbyAQcTRjpNNjpW8QDxuTLxFn7J0mwGpdQ0q30ghAmE01y34W1fmA7OCfYDPchooiKeHfODrIb/UwPkguTX28FYQPG0Tfwoj/JHRma9oD9LCak5BPBs1JE/wq5Qta6sNJXzrPPyBe9oY9NSukHS//61QRNHKq4TOPHtehr8s/ekQP4WaLuI+TK0k7kkA4yCYSj8/pEHCaOYLla+Bg89KdneEjD2dTXFnkFodTXHDx0dN/9zsMLkcYFhDtYflfmrFMSyhUgqiMIFyVkiZYS0GkIFI+JWQCFwKNHiAmj2S2FxBiFgCGbSKYZI7c9ckJQQoj7qqYsdTCAQlCd852SIOjypkQsUyK+PIk8lQagHV9DykT5FA5SbEOO911pF8FXhsM9hNISuJ+6CuKsHuoltsczQ4Lebp9nox1Rziw+I992eX3/u7+PUNrlrd0wdvBOUxyWH7qWvOVVJ/97DklB7pGSiJNq31s/t7a+q5O+NmnikVNnhotcr2Z9ETUzfZ5xRtMYY0x5j2wMY1zInD4w+RGCwGtsEhCemkkxNJ71E0IhxMMEkZEh08YWHYCEMGh2SI6KZZq0/K77jTPlmLga05aw6A1etcDBhMcEwI8lIJ0Onnp1Np4kn/Iie/JjxMi8iSScybjJKV3lOfc5x1vNANNpy5jILIIEL94h+pBxRx5NCMMbyVvpgCPyacXJ6gQMhnRnFybKnJRQWvZEeD3XNc6MSbZCX+lH/Se+T1t40PSTPvKsT/GT5Jg3z6TMmJDHUPKsccdBQceZPKnTSg/PI3LqNqrsUfXSZuQQTuL89RPS1IWVc+ylsYxM8tAZI+RdX6qL60i5X4Wz2YWuq1fyYGi82/Czww47FI+18rqS8thAtrceK8YcvamfhCXUxN4z6qEcdepqR5QlnyCUCCLSqi19zzgfhDI8lBFjGnnO6jMJ5QqQRKzEWBggZhjhldBpliEIueVmSsqsgzEmqIgdxUxgQriQMYG4FAGXNyPWJxhR1VAQCJuB1XW/c8pQN7MedRVPyQipozy6kucQHu1irHhOEWeKq1YC7iOUCJaZsgHpnHsoN4PWTjgC5vw4yX3qLA8YzeKQlzzHrYN6urePUPJg8Ahor3zdZynBZh2z1sDBNYYrFEqUr02UOaUingl2QzPrcXBbjXvINvLMC0YekLdo62qUrx/JujFCiSICcEd4gtz7hCXSZ9bO0IhVEvIwKcb6yzI3wqifeB9NHI2bkFF5Ih2IKwKLgKnnPJP8EVoeUsXHIYcAACAASURBVHoj4rnIYeBAP9EjvC68mQykd8shBvSBtgVRIM/iwHktxHbxYpFNussPIPD80F0mw0iskB67PY1xOCxb0nYY0ku8eLxRcDTR179wcehbBxzpT8ug4tSFNxjDkyRlTkooTVJg3Uco5ake5NE4tIRtCZTHSp8iR9E/2oFwkVPLrNpKN8X1vrYoI2wBIqj9PLYrPTxPduiRUWX31cl59TLJYXv0Xazs9Y099+tHY8F4MXY8Dz/XIk9thRPSGPsi4jqcg1BaFTDu+wil/LRPv6hTnQfdxPuJH8ADP+ird6lYxx9514TSpt5JCKUJAVKvfrNOSSgnRJRwUOheKEqgKdZQMATDTJArGxGjkP2P+CF0DFsQRkJB4VPghIsCIcyjjHSUbwal/D6FE81Sjvry5qgTQ2zmNEQoCYV4G7NZnlPlEPwYGPL2neFynsLyf5zjJaLcEFeDtH4u6tX1CUdKHQGX7ywOeWl/9FFXue1z6jsOoXQfHHmGYukplBRM9Dvlqw6Bgf7nCeHRtIxaK/52PeL/eDb+Xxuf5AihsxwX78eDq/OrkZSjTxgDIRiWnnglyVc76QOeNpMaMoww+X8SxW0c2sQgJo3it9TXliF1YiDkf9hhh43Vl+26Tvq/MpFoJM+GOMuiPIxdxoFsIsXqBwc/wBCeKTLJwFpF4cnk1eLJ5H3mhWNQhcvQF3QXz53JsDc7MOB0VRf2k7ZnbdxvPPEUCTVBKLVb+0L36meyjVi61xiFkTg7cjCJHGmfPCYllFa4TNyUPTT+XaNreO49w5mhb9Q9EnnRzxwEDs6C0FNxT9+n/OksupjsWPpe6eF5csduDLWpry5x3rPqIFzIEj45ZYf78jRm2BUeae2P1/zVGHhW//NcWzViu/Rz5BmEcpwl76hn+1OfiMX1BhjEn4c1+EB9b5RZn6u/a08QShOEepW0vi++yy88lCbjPNV9OiOeWelnEsoJkdM5lAqiIF4JgDpY8snAUM42w4jHQK4YG4qaEIQQ++R2N2tEKAmq3V6jlJUyDCZ5W4YYtfQoP8rsoIMOKoOPMJtxK78ryV/7xBZZ7jYAw9PYvh8W8o/2G3S8NLwddVB4+7m+/w16AcsGNDIwi0Ne8mQgxk3ahbyI2dMv8KYM27/lLT/3UhQ8kWGAwpDzWjJWnnefxEB4bYQwArLRpVzdC1P5uV9/UMSUj3P6HEFyjjGpFd9QG+VLsZAHz8sbKYj+c11eypBvLSOuaZd3myIoZrk8tc51JffH0XV90nPqghzxnCGUCLw21HWMPLWHLPGGuBcB5lnStnGSesOA3PBwGgsmYbw6jI4yleE+htbEgCHyjHN1Cgza5+t7JvkOb5NUcq1tjCS90tU2dSQrMWnVFpuVQj7FVvK8us5zgzCRVbLP46WfeV5t8IEnuTGhEPbA20mWVtIuz6iv58mZ8hwmLF3tqDFcSXltfOGCHPvJXCSMHg35UD48ebiRCv2t3XQgJ0IdNtDOt+9/dZYHvSi8if7tayc9BWOGn9wZ4+rbl1zzjNUvu4Z5o4U0xbhUNhk1XnjzXBd+NZRnuyz3kvlZHNo9bR/KgxyTWRMlY0Gf9SXlGbv6kK31HBLG40gO9A05JI/yMaFCwut6wtP9lsxjU87QhMqz6lm31xjiCLJKaCJDZ8jXvTCmP4w/tqRLr0X73KuenDbkhKdd++r6xr0+nVfX2JRjgkgGQ0bqe6f9noRyAgRDSCz18jAhHPUMw3VGjhLmDTR4dR6BD+XkHgfhFZNlpmK5G+msiUdftQiaJQMvQBULM0rBKYfhR/CUxWgQ2D6FIn/tEz/pfZqWDXlqDLihJD/GSzwHbLTNM9o6TnKfthhwyArPyywOyws8EQR93KQuNaHUp32EUp7ud8DAIGUEzD7F+om30a+uOSgw/SYei2LvGtSUkPpSOPCw1HjMMccUz6alFoYNueWl4o1gUJQ/lFynVPQtgyXujocKUQpSq1wkUxn63Pc6X9+RWxsWLKF27XR0DxmSp/4PRV0r1q56xnOercuMexl2Hkkv2OWZg0ffsp3n9R8ji3R555sxI+9xkudhRYb90IBJlWVg/xvHXg8Gc/eog81ZDJD+jRTtmRQHeahnH17K5OUwUUEQTFRrHRTl+1QH9bJcyZAyhLFkSy9og1hp440nNrwW8hPAb7JLXzBe6qTv6TNx05bM9O24mEa91EnZSI7JCRmyOcKGCDJXh/B0Yaj9fdjUZRhXfXXzvLLpZx4eY8EYZ9BhwltJ9/hUV3Xi1WOEla9ekyT3j0soyZOwDrFuPMF0h/r2JfJinBqT7Iil+Tr0AgYIEjIrTyEc5HfSNvSVvzbO0wVIM2JmQiA+eEi/a6txaL+Cl5/baGrs0AvkmX2gR8k0vaHP67GsjXDk8DHJ8py3SZCXrqQ8YwjODmXLT18i9mSOXgli735tUr7xFrazK2/nyAOPrDFrZZNjakgu5a+ubBK8TLSNvyG56it71PkklKMQqq4TCsqFZ0mcBSVD+HSY5BNZQxgsFQu8pfh5AmLG4x75mK1YvjPDMtDNmOKeqsj/+epZSpwS9goguxMZBMLYTu4lMIwBFz/vBKJH+Oo61895huDxSJhBIZRmZEiQMlyvk/8drlGYnqMEDTrP9Cn0Oo/6OyMQ3rcYjNN+MhSEvIu41WXX37WJYUHmeCjlYQDa7GH5n4IODN2rP+VPGekPhI8xRnwoLArfPXBC1mAqJo03uz2o5QdLr6gRQ2VHIDmRD3JqVooA8DhTSiYvQx7naJf6Uli8o/JCkOS37777FrKsfvqeUddmm1Ao7Xb91I3SQyjVkeKMJA/yqRzXePi8AJ7ypQBDscb98anNDKnnGMMuefYspW8JFlFnHJzzbDs5Z3wxsrw2DIB+mUQetdtrSaxEUML6kvI2EeAlNZmElXbBQN5RFzg4h6AhZoGD+iNllL972sk5cgcHct+Fg3zlKawm3iQRstiVn/GkDfRREEptU1fP0Qc86TaemAi5ZryQObLGq6Fv3IvQm2CaIPvOi2lcdLWlXRf/u4/+9KyxhJiSP5NpnlaGPXSg+oUsIZswFCsWskRWA++6LOdgZBId3tj2dW2hQ3l3yIfJNi+Pcky2kC/63SQEHg7l6Y9x29ouc1xCqW5CaMgawsAbPaS71E2/kUkkCWEJEqquIS+IFDk2ATE2ljXpX/JIv+g/7RKeQVaGEizocXJN/7KfJptsL1tHpwhRY9Pl1e5n/1shCW8+ckbft5P6kRVj3Qom8oY4ylPcOa+zfqLbyad8PUPuOWN4zW0eotv6EhmhW8Ry8ngPrRTJQxn0gLAIk2NjzRhvt7GvvEnOJ6EcEy0dDCxkQkzVmjVrysBnACgaQuHwPwLHG2gmYjksjAPFQNgIok0uiCdPph2iZqWIR5+xUU358+IhBTwODKUBQoEQMsqFkDjUCbk12zGA/JQSZdL2OsmXMVQvyt6gQZrEptjEI+5K/V2jnAyMOJwjqLxeFD6Ca9ZouYjSWonAamO0YVaf8hwnuU8faZc2wxkJRywMfgOR8eG50A/6Wjt9p/gpOdghWxQWBU4W3CdvMsRwMcq8Il3GWL/xQFFuPIgIFu+ovqNA9aM4Nv3KW4cUjlIo2q4OZIzM2XyBEFkKNuEJYqstSLQlIeVrd7sP3aNeNqrIw5iIpH3IAqVrSR+BQXrUEXa8QmRfnnWfkFvtFLdLEZOzdqJwTboYWvJs+amPIMoPtsiK8cXomOA4P25SP4bL0i5jDSuGwKEfGCFkXp1M2mqcjA9ki56wkzNw0HeIFC+nvNs4aA8Sw/OsXPnUSZ2MX/FwcOBF4x3tw8F5BBZhFIITS95wkJcxj6RpB6KmPNfIPu8u4sgY0kn6hNyRF/1uYwfP+bi4Kk8+yJJVA5MhkyqkXHlIEdKlzu6lz+DEo+ZeGDrIknhGxhmG7T6FqTab9Jjgkkn5RXI95JxHzzhFbJXjEBKkb8kOvepZR7uvIr9xPj0/DqGUl/bwIhuDxpgJqH7qS+pFJkww2RyTKHjSY9pON/Bacm4gH6NeyN1XzqKch6X2ImomA3QpPTVEuqP/3MNeIY3GGP1ujBqXJowO3nx7G7rGlHJN8JFP8uV/eddJf7C7ZDxWR+h8MskumhDTIeFQUI7+Jevq4lds9F9X+VGOsagf6RZ6lv5Wbl8iU+w6gktv0N/yaNe97/lJzrMHVs+soJr4tnXYJHmt7XsvNK8KAN7ANovn3cHydQwFRCGHh8h9hENnI3u8dZYtKVIHY+Ear41n5cEL6BMRMesh7H2dTWgIG88BQikAmwJkYAgkBUiYKWd1siwo1tL9iA1ShLDUibDxslDsCIeBSvH4CUd1Y2y0EVlCRBxm8gyQdngPJ5KFOFDQDA5SYfDOQ2Drus/6O2wQQ8bSRMCSGIztmvT+RVhTJowNY4oAUQCUOK+hAa5feIN4kMhAHSdpcOkH5KSLdMNL35skKJ+8MKwGKc8JuaOIKDxGh5dOnyG0QwpFvgiWGSpZMQHQJoaTpzI8U4grb5F6izciS+0+REyRKEYeJvJ1j/IpNkoacYEHpU25IICwQVIRRnkEEfAs3MkMsut6Wwm5h4ya1SPpCKtx0NdmhhR5JfcmRuqibX33d8mRMtVRfxg3XqKOSNEBjAVyQ96ND2XFuPKMvmGY4MDjYEmOARJ7yABop7dEINc1DnQEPcOwxI7sum7qRD6NN30X3ijnuxJdRP/wZMBBXYL8esbESR/xhCHoxqz68JAiIcgvGXAOpuoMU7JjguB/pG6cBHt9oG0m2jZU6Guy7DzjrAz3Mab626SLXoE3uYUh7zlcyZL4T2PDM9rj8CzvDYxMumBaJ21hXD2PkFhN0Q7LmciuiZs+MrEyiZE3HJUjrz6s6zLa3z0zLqF0Lz3Ok6Xt4bVt5xn/u18fxOu09A/Z4Y0zGUA0kWa63IRfG+mUZU3ai5zRKSZJJtX+109dyf3kSl8aoyHL4QgwtuloconosV99HkJ6i24mf+SG7Mq/TupBn3LEIPFsOz1NFo0//UP/4wYmnfrJJ33Brhj/6tvOty4DKaaf6SDjtEtP1/cb1+QbZiYpNouuVJbrfLu+Gyf0RhLKLnT+/zmdCySzY4aRQPEqMqoIW91BhJZCs5OKsdf5njfo/c+4MSxmVjwnhMKnWZK8GHQEoUugKEskgzI2qybcZkyWJ+WLpPKmIB2Mr8HGi4EMMsDq1s6XsFn2YqQJHGUasSmMFsFwXt2U6eDxkK9B4f1sZtMGjnYwUIxWu5wBeBfmkoFMmcASDmaT2oZYRjuRbZhQPA7ntdt9SCQc4Kdf9ZPZs36DRxBK8sPb0544uEcf8V7zLLju2fAa6w9ElmKjEHySq1HkXb7ICwNDNk0+9Kl6BoGTh+t2lqq75ZcusuCcupMtM14KUf6e51kNwoEgUqzIAiVp4mHskCMTEjICb8aabFKQZIrBa5MAShom+gGp5qFioLuSuhhzJlP6w4QAGYRln9Gp8/G88o1BhMsz2qbv4I1AU84MvTFsghFeH88y1jBF0kwKhA1Q+EgCQ2/CBgdt9ZxrgQMDpW3kT1/Uhl/ejCFcyRuSoI0MZVdyP5KGhJEbWDCc2uGag6wpx3n10FaH54wDMqgO7iWH+tNSMc8fAsp4OD9OUhbc6E2GFgYm0fCgw+gNxEBZykSEyagNMwi7OsHQhBvRND6RS14QskSO4IiIehUbOaPXlFsnfWmViSwhlPpIX+tz2JAzfQNneTrv1V6wpnvHkaG6PN+1aRJCSc54kcgWvWwMyaMrOa+NxjWiTychJtrFHiAQvPTIlwkIjMfts67y1vY5+JtUmciZJCFUffYSNvrbBAmpI7/OxSEv1+lGY9F4JlfGr75vJ/JFFtlvpNDYhqX8IvlOb3AAyIu8m0A64K8c8s42CEXRT8issUB/6vs6v8g3Pl0j48gpmyOcTnlDz6i3MU7mTbTIOF0yj5SEckxUKRPKjHKx3OLTQcAM0uggQkpAeYEokRi8Br1lPQbTc5Rd5BGf8tXZBKArKUMelqPkw3AiG2JIKGTeJh5UBomniCGlaAipgdMldOpLWRsc6qSNCADhdlDsUb/4dF/UP+73jPojGG0l3tWWRTynrygdhk+7tD0+fddGR5x3La7X1+IexC1ICeyDUCKkXYQSJu7TVzAMhadveJkYYTFt5MM19WV8fR+V3CNPMmMiQtExPggvuZIPj4/zyCLy0yWHZEl4g8kVZcjYyZsCNgtnzMiJ/+XrkA+55WXSdiEBJk5m5mSGIUSyTYLk55lIgYd7kSgYIBFdZDfuRZJ4CCxNGwuImjxdH5XcZ+wiYkhVECrPaqfrzsmTMUO+bSoJoqa/KXnGwhjVds/oK5gwhrycDJIJqe90CByMX0rfhJAcei6S8pXLMCDViAJihyi3U+CgfMTKUqj6GJvtPMka0lSf9925WmfIM85rU8in8+Mk+JBdBJ8hRNAYV8YbSdW/4RHUJpgytGQJpsqGobLpPB45smpCx1NuTCK5EXJCDt3XHhvapV9NDHniybK6Rd/ShfSmcaKN/ldXZIDnuZ3fOG2H0biEUn76WbuNCaQ5iEtXWTCBF1JlLNNH8DSWfSLflnKtnBj3xs1K2tBV9mqeg6FDnyD7xg59YKxpf5ccOucavcTRYuy12x55wpuDROyssUhO2okMmlAa9+F57CJz7jPJMa7JuHEqTIr8uN9KDnmPfiK7Yrzph3b92nUwJtkXE1Z6lK2CSV/SPrJBnxl3eEEQ675npjmfhHJM9ELxEjQD3md8N6gj6cBQfoTD/5Lv7tP58Ww8H//L1/U+oZIXoSOsnpGfelGIPAw8PVz4DrN5xFaedT2invWn+rqvblfUaZJPdVenaHNdxjJ8j75r99EkGNT36hvYytehn2LJmxeFkan72j3+p3QMzOgP5M7yF+8wBQJj5VBMdmmTCc9KPtVfWcpv970yzZB5JxG5IHAUL6+kJWWeDbFxnm8nS0W8cwiNWXiQAOXx1DPuZK/dLm0x8dJuhpIx9+lQF7N+Cth90RZly4ccM4aIEYLE4+C+SIGbc0gTbyfiIu8gXZGnT30CP7jV8uqaPIRzINVCUBCYeFZ5vquTtiCACLIX2ke9GWxGzlK/cdp+1n36zYoHUgkHJFJdeRF5/01E9GH9rDJhjdzAgaehvSEv6qYMuoBXmHeS4YkJQp1njV98j8+u+6L9rvVdV8+Qv2iDe0O+9AvvjD6EFVl31H3hf6E1wklgqI8iRf76GanUNp48+MGRoV+zZk2RJXnWST1gSD7IufuQk5AB19VZvci+T0Yf1vHi87521+W0v3tmEkJJPulwpJiMCDGBSbtsWMCHx9/44Hk1Vtyr7uQMoSQvxivHQ8hpu46L+L/26gc6h07xqV0mAfqcp54XvUtPaQ8cyQmdFF5//Qs3eTvcI1/hW8YymVNWLXOBjfthK/RF2TzZvIXyqFPkC2t60TNkUT1dkze9Yky45ntcq/Npf1dv8qtP6YqY0LfLj+eiLGPNxMsqqImGcueVklDOC9k55UtIHHWKc12f9X35fe0hoG8oGATHTmFxl21lRGEwCO7hcUbqeEoYTgrEzBhRoJAoSnE3PDJmnKEklSF+luLg2aDQankxW6WIzFbFxvI4yg9B4vlmfMxmgyi2EXNe3SgoIRwUvPwpTB5tO9zd007uofgYVvchs7yuPJ2Wb3jS5VHXVR6e0VYeR14WXkyTJ+fdG/lSygiWuFbKHsEQDE/B1QpXW3mdeNjFzumDUObyomzF0SHw8KHwYRvJ92irJVkeDR7RyMPzEY8M23aK+qoDImpJl6fYWwEQ1FihcF+d1BtBQDjhAD9YtnHQ3zysls61AeFCztSlbked96y+qzNs9CUixtirj3LJJc8Pjy6yro7a5JnAJP73DGzgH/LVrqN79S3vDiwsgcKQLJl0DU0EhGWIfxWbGV7MqIe6wtTzCJpxylNqLGnDSpK8JyGU7ocBPYDkWPbW92QsUtzDW28sw9UEDyaBN2wsuyLZQZTmLQNRv1l86gcTXsv/Xl5ugqe/bBajp4Tn0A19bSIjZBGRoj+NCTiSUdfgSb/SlWIihY4h3Sab8O1K6sTTSR/RMUMTtXheXn359Z2PZ+tPZNi4IbcmT/UKSn1ffJe38WO5X/gWR4EJiDbMKyWhnBeymW8i0ELAQGZkKUJGzXeKLRIFhwzyTDAkDAriw1havmUsKTNK1pKgjQWUShAxihVRQQx5vniC3F8rEIbNcjMDZAnabNxhEwKlYzmVt4NBays7/yOvPJwMVewMdl4ZnkEma8MXbfPpPu11n3wod4dlJEq8rqe2INcMBoLGI+EVWwiYNnnGgRAyGrBieJEzCpfXBhbKq9sBq/BA8v7wGCPZylO+PBlfOCgL/vJBFB3qbbkKmbdEZjktyFrggITyetR924WD/JQXOFD2cKgNpO/yR/i10VItMiR+1bJ74GCigdharkWAkHVyZMmN0dS2Goe6PrP6rq7awvgjOeqBIDOEykcGyD6Sy+umb/WxCQLCxltINuDGEAauXfULrN0PN3mHLCGD6hLtVbZyyDlZ4iUVPwkf9fO8Q9/C2bjkbSdPDDevcGDYVZdR59RjUkKpzvqXnBnHVg+MrWiT9jHePP7kwXg2wSN32m+MmDgiUjyswgHI/jIlBN7yrj6wkQu5R5KFAdBvJi3GUGDSbhs9pN0mrSYbNiXRjQikfCxDI+GIpBjHCHWAbV9SFrlE5OlA2JK7Pp3Xl8+k50OG6GltocPJai3n7TzVCQGlpzgxkOX2RKv9zLT/J6GcFsF8PhEYEwFKgVEzUxQLyFOHQESiAHjZGDqGgNKwaYGBXrNmTVGkDLElF8YOIYzNFPJghJAfv7dtxyKlG8unUQbDKn7T65AoUstHSJV7vfDXbJ7nDgloK2r5I7zi8Sy/U9Ztr037mSi3/em++mhfhwt8eGeREB4JRoUxYeAtu/NcWDqGF6WJSFpq9iodhER92wkBYYh4yWCElMerb7QZOZWPAH0eSN4QGCEYSDyyzfh4jgGv8a/LmhUODID+0GbeSTiYXJAJZCJw0G7LekIHfPIiI0YMbhcOdV1n9V05iB1s1BOpRWhhDg+fyLjrQhfUnfeUUed9t5SHuIVcjFuvuD8+288pN2QJbvrdQWZsclMPhtq4ghsvOC83zx4SwgtFzuW/kuS5SQhllKHvEGBYqRPSHeMSkUBsvC3C5Ee7TH6QYh5a3jhxhs6biBgPQ0QpylykTwQY+aML9Zcd63QVHWVyTZcOyTas6AJxyvIxjnmz6U6TRRMe8creWCB0iG4cyi+wcY9JfcTrWgkJuY17ZvlJfsifCZexQx8ixEMTBH1N5ugIxNeqk0nTvGUgCeUsez7zSgRGIMCDYFmYJw0JqBWRwU7xM8K8ZMgSY0eRUCCMnQORYiTMUJHQMHQ+KRkbNyhfrzlC+mojpHzB+8iQ8uWnLPVB2JDFrs0HkTdShYxRUBR6nxduBAwjLyNSZtQMiLKQa7FQviNXFKXDK3m0QdwiAsXb6FlYBi51YfDi1eTxQRyRr9iYFDN6eXozgvIRDQZJ+bwbsII/YqTvavzrcmb1XX+SB22NesBB33XhwHMiZooxCW9nFw6zql+dT8gv3HjWGD448thKDLHviBHyBmft0sfkkdzBc9YJobSErt/ID/wc8DQpC1kKeUIyxZ8iZeHpnsYQw38lhNLYIs8IC/KrTjCK8Yxk2EyCNCIaJl9wRXRMvNxvkw7PZt94mDXWs8xPO8mKySuCr42WppFBE2XyPSTb2kzf8d7Tq77zisOQrhDSYLPgKJ3RbpMyYe95RN8kbp6kHQ4mvYivfjWmELc+8qt++pze4DhAoj2vzvNOSSjnjXDmnwhUCFAO3hjg1zgQPootDITbKEGeCUqO98sApTgRCwSGB5Jxcg+D01ao/qc4eSvs+Jc/Iy1fM3Bkk2LidaFcHYyOXYOUNgKlzHZSFgLLSFmCo0DVqV1++7mV/q++yIc2O7Q5jjgXn+rLYNQ49pWrvvDQFoQGaWdwKGdlyscyMqxgrAzX9RniaXlLPYK09pUzq/NRpzYG6hDtj084wGwcHGZVvzof2MKRMeMRsqOUbJGTSNpDnskoWSbj2jJu/0U+k3yqk76ET8hQfAZ29WdcCzynnTTBRZ6jfsu73SbPKdsGHbHBiCPSSze4Bkt1jKVbY9fBa8mjzlsMe/ctY1Jv/WIyYPJk4ogw8yaS83Ha5R79D6/ADKbGiCOuTYqPfNXBkryJKY8pck8vzDKpM6+zyQJ9rv0Re+9aVzK+rGrw+vPAxtsm+u7vymOl58gjnSpky+TWZG5Z09xebL6sgGS9Fw+BUESWnS3dWOajMGrlGEqQsvM9FKH/44jz7Ra613IM5etVGYyKZyjRiMmyvG3DA8Nkti4mUlwZg4eAtmey8mSQveLFMrBleEZOvvNMNQ7R7q5P9/Xh0a6ftiCU2gmfesNKjXPkGdiFAfKsc+5drTQPHOZVd9iQZ4aPN5f8waxONc7Rn+P2X53PuN+V11VmlN33GTIwbV97fiWEUvs8iyDwMBmzvI+8as471B1pRNCDCFsKZ8jhPk9cx8V/pffV7UMsTUBMVuAxTbsCu/hcSf2ibrAWTsGbrX9MRGeZtFOYEY+/zTXGE/2s/K7kPIwQUKsYk5DvrvwmPZeEclLE8v5EYEoEGAFeL8RMsDSvIcJGeUybKFveQ7NmSx48P/JFKBkige1ikiwzmlGLJ/JKEb/MwWvJENX1oKCQKZsX3CvuyOt9lnXmqW2UHuUv1tSyFcPbp6Cn7Y8L0vMhK3ZzixO2yWFWcr3MOMJlpYRSuwNXRF2MrzFdy6vvDrLtiP+XGbO67ovcNrpcf9CtJtn07yyTtiOI9BRCPUpXuV8dbMpSp9X2UCehnGXvZ16JwBgIxKBH0myM8atKgsenVUbyZcC9XiM2MMi0+QAAIABJREFUjFB4zjM0SKDlI+WtWbOmvPImNuaYBXvWvXXyvGVxs107Gi1lUqDOL2NCrC13i7mEBY9Cu83L2K5FqDMcLQNabvNeP55ycndBT3CZhlDCTx7GHPlNeV0cidIXZFzfOObRN/LX7z7Hyd8986zPEPpJKIfQyWuJwJwQoBzE29iRyfNnw4Dlk2kTRWJGawmsns2G4kMGeUctfzvEBLo/4ona5SO5Xk1kk48dp5aexlVs7bwW4X84WDKCDywo6kyzQSAMGVxDpmaT83LnApdpCeVyI5C1v6AgkITygtLT2c6FQyDIjY0JDgRwFkm+jq7kPEJYH333eh5BRXQtt7WXw7vyX5ZzQxgtSxsWtZ6J7f/2DDySUP4vJvnfuolAEsp1s1+zVUuAAEOD2K2N5Ylxjb77on7L7JlcAnHIKq6jCCShXEc7Npt1PgSSUJ4PkjyRCCQCiUAikAjMBoEklLPBMXNZfASSUC5+H2UNE4FEIBFIBJYUgSSUS9pxWe2JEUhCOTFk+UAikAgkAolAIjAeAkkox8Mp71p+BJJQLn8fZgsSgUQgEUgEFhSBJJQL2jFZrZkjkIRy5pBmholAIpAIJAKJwP8hUBNKPyrg/bNeXeWtDnHY+Oa+TInAsiAQG0q9ei3k2MvXjzjiiGabbbbJn15clo7MeiYCiUAikAgsBwI1odxoo43K72wjlXGcffbZ5WX73kubpHI5+vSCXkty6kch/CToWWeddZ4s+/U1v5K15ZZbJqG8oAtJtj8RSAQSgURgtggEodxnn32a9ddfv9lkk02arbbaqtl6663Lse2225YfDvBzefmi/dlin7nNBwEe9lNPPbXZddddizcyZHmzzTZrNtxwwyLjJ5xwwtL+RC/ULjQf6DLXRCARSAQSgURgZQgglH4c4Kijjmp22WWXZuedd/6fY7fddmuOPvro8qtVSShXhnE+tboIIJSnnXZas99++51PptesWdOYPJ1++ulT/5zw6rbqf0tLQvm/eOR/iUAikAgkAguAgHgzS9p+lrJ9+P1zP3GacZQL0FFZhbEQMEnyc73nnntuQ37bMu080knulzUloVzWnst6JwKJQCKQCCQCiUAisCAIJKFckI7IaiQCiUAikAgkAolAIrCsCCShXNaey3onAolAIpAIJAKJQCKwIAgkoVyQjshqJAKJQCKQCCQCiUAisKwIJKFc1p7LeicCiUAikAgkAolAIrAgCCShXJCOyGokAolAIpAIJAKJQCKwrAgkoVzWnst6JwKJQCKQCCQCiUAisCAIJKFckI7IaiQCiUAikAgkAolAIrCsCCShXNaey3onAolAIpAIJAKJQCKwIAgkoVyQjshqJAKJQCKQCCQCiUAisKwIJKFc1p7LeicCiUAikAgkAolAIrAgCCShXJCOyGokAolAIpAIJAKJQCKwrAgkoVzWnst6JwKJQCKQCCQCiUAisCAIJKFckI7IaiQCiUAikAgkAolAIrCsCCShXNaey3onAolAIpAIJAKJQCKwIAgkoVyQjshqJAKJQCKQCCQCiUAisKwIJKFc1p7LeicCiUAikAgkAolAIrAgCCShXJCOyGokAolAIpAIJAKJQCKwrAgkoVzWnst6JwKJQCKQCCQCiUAisCAIJKFckI7IaiQCiUAikAgkAolAIrCsCCShXNaey3onAolAIpAIJAKJQCKwIAgkoVyQjshqJAKJQCKQCCQCiUAisKwIJKFc1p7LeicCiUAikAgkAolAIrAgCCShXJCOyGokAolAIpAIJAKJQCKwrAgkoVzWnst6JwKJQCKQCCQCiUAisCAIJKFckI7IaiQCiUAikAgkAolAIrCsCCShXNaey3onAolAIpAIJAKJQCKwIAgkoVyQjshqJAKJQCKQCCQCiUAisKwIJKFc1p7LeicCiUAikAgkAolAIrAgCCShXJCOyGokAolAIpAIJAKJQCKwrAgkoVzWnst6JwKJQCKQCCQCiUAisCAIJKFckI7IaiQCiUAikAgkAolAIrCsCCShXNaey3onAolAIpAIJAKJQCKwIAgkoVyQjshqJAKJQCKQCCQCiUAisKwIJKFc1p7LeicCiUAikAgkAolAIrAgCCShXJCOyGokAolAIpAIJAKJQCKwrAgkoVzWnst6JwKJQCKQCCQCiUAisCAIJKFckI7IaiQCicByI/Df//63+c9//nPe4f9MiUAikAhcUBBIQnlB6elsZyKQCMwNAeTxH//4R/OTn/yk+epXv9r87Gc/K/8nqZwb5JlxIpAILBgCSSgXrEOyOolAIrBcCASZ/O53v9s89alPbXbYYYfm6U9/evP973+/+ec//9kkqVyu/szaJgKJwMoQSEK5MtzyqUQgEUgEClnkmfzOd77THHnkkc0222zTbLzxxuXzGc94RiGVf//735NUpqwkAonAOo9AEsp1vouzgYlAIjAvBJBJnslnPvOZzc1vfvPm/ve/f/m+zz77lP+POeaY9FTOC/zMNxFIBBYKgSSUC9UdWZlEIBFYFgRswPnpT3/aPPe5z21ud7vbNY973OOaz372s80vfvGL5uMf/3hz4IEHNre+9a2bF77whc3Pf/7z5t///veyNC3rmQgkAonAxAgkoZwYsnwgERiNgLg5BxKBeKyNpHxlRx3mEcvXbuc8ylgb2I1TJly//OUvF+IoZtJmnL/97W8F83PPPbf50pe+1Dz5yU9uHvWoRzVf+9rX5kooV6MflDGpLK3kmXGwz3sSgURg8RBIQrl4fZI1WgcQQOSQi1/+8pfNb3/727mSiT641AGx4TH74x//OBdiizD89a9/Lbub//SnP62Vdva1f97ntf0Pf/hD85WvfKV4IGMDjvMOsZM//vGPC9GEv3PzSvpaP+jr3/3udzPvh7o92vyvf/1rZFM8Ezvff//735dn5onByArlDYlAIjBXBJJQ9sCbiq8HmJ7T8GLUHLwY9RHnfQ7h2pXHuM/2VGvVT2uDtiMQn/70p5uXvexlzYc//OFCLietjLwYbkRBfg7f5T8KR/cgFqeffnrz0pe+tPn85z9fjPukdRh1v3J++MMfNi960Yuat7/97YVAq/NQ/UbluUzXySciCQdH3VfIPELluvvmkeAsbyTvox/9aJG3T33qU4XMzrI85fzqV78q/Xz88ceX1yKN6mfPmEy96lWval75ylc255xzzlyxmGV7M69EIBGYHIEklC3MKMH20bol/20hAC9GE4H5zW9+0/z6178+3+G867x27m8n5xhf97Sfr5+dl2Fu12el/6sfTx0S+bCHPay53/3u13zoQx+a2MDDg4fL+wzPOOOM5jWveU3z6le/uhBE5+Ddl4JMnnzyyc2+++5b6iG2D76zTsr6wQ9+0BxyyCHNXe5yl+Z1r3tdIZXOX1BSyC5vtNjJ1772tYVEffCDHywkap67vMmbiYayHvjABzb77bdf87GPfWxieRvVV9poXHot0u1vf/vmxS9+cYkfHfJUesZ4ft7zntfc6la3ao466qgy+RiS3VH1yOuJQCKwuAisGqGkXCg/yoSCrQ+GjgFyzyTJ/RSa59v5KWcc8iEPZXvebJpx/Na3vlUOnhfn5K8cREEQvnu78u5rX123cb5H3SfFYxLsZnmvdjOmL3nJS5qnPe1pzWGHHVYIxsEHH1w+n/jEJ5bzNif0ecr0AQ+GDQ5PeMITynNIiu+M2POf//yy4QF+i5rgwCuFvD34wQ9udt999+Ytb3lLIdmT9KV7tfMb3/hGMdxIoV3DO++8c3P3u9+9bPLwAu2uPNXhz3/+cyGx97nPfcpziCXS0XX/tFjKk1eONxaBRirf9KY3NZY41WVdT9pvvH7ve98rXrgHPOABzV577VVwuOtd79oce+yx5do8CLay//KXvxTPpN3le+yxR/Oud72rkLhZYx/ttLz/6Ec/upDKl7/85YVkKsv1rkRv2gV/+OGHNze72c2ao48++rwNSn3PdOWT5xKBRGDxEVg1QknpWJYRqG4W/5GPfKR4XnhfzKgpHUZ0XCXjPkTPy4PlJx+HfD/xiU+UuCblDeXnmjLtwFQHy5MPetCDitHec889m4c85CHFSAi2R5gY5oMOOqiQzraBkBcy8bnPfa7kZfkp2ujT/8roOuJa3PeFL3yhkJB2GYsqTvoW0bYBgUfuhje8YXP5y1++uexlL9tc8YpXbLbaaqvmXve6V3PEEUc0n/nMZzo9ZdrKKO+///7NFltsUZ737CabbNKsWbOmedKTntTMYylvlpgyntoAh1ve8pbNs5/97OJhnNS4w4K8veIVr2h23HHHIo9eS8MzdKUrXam8jgaObfkIeWb07TC285hX01LlpHWYBBflIqzGxy677FJIrDHY542eJO9FvxeuvHBI9E477VTazxO32267NVe+8pWbG9/4xoXc01WzTuTNpOPxj398c5vb3KZMPsRQzrOvTR7o27vd7W6lrTyj9B4Z6EvaTiYRba9Wesc73lEmPfOsZ19d8nwikAjMD4FVI5SMH8/foYce2iBrDO51r3vd5trXvnYhELxRlOGQYqph4BXgQeT98jLh61znOiUfr+mguLxUmLJtG93IgzLjyWGYGWvG1wyfIX7Ws55Vnkdudt1110JyeBoYDB4YJLadr3oLwOcpQIDMxjfbbLNmo402KvXy/53vfOfyPKMrH3n7ZAxucpOblPs233zzcu7UU08tnp+o7yJ/ajtDg3gzNna8IoMXuchFmqtf/erFc2XZ95vf/GaZVHQZEud4gBFuMnLVq1612XrrrcurWN773veWXbKL7PVSf6Tq3e9+d5ETHsUvfvGLneR5qC9hiYh98pOfLESS/HgdDe8uGbnMZS7TkBE4t2XQ/8ijZWdE9OEPf3gZA8bKvJOyyb+x45dieKp/9KMfna+O867HauePLJkk8wZf85rXbB760Ic2PHd0hQkVHXfSSSeViess60bejIcTTjih9LWlbjvO50Fc63orVwiKyQ6y/NjHPra81H1Ixsg0Tyrv6bbbblsmnXTFIq821G3O74lAIjAeAqtGKCkiM3mei7e97W2FpCEbF73oRZtLXOISxftiudl9oxIFZSlacPj222/fXOpSl2oueclLFhKHSL7zne8sRFF57m0n55BJcW6WqLbbbrtiCD7wgQ8Uo0hhOvz6hWByJPUa17hG8ZrxtPFotuspT+W99a1vLcb/3ve+d/FQIFVXu9rVinHnLRJfJR7O8frXv74Yf56sRz7ykc1tb3vb5gpXuEJR1O95z3uKEm7XfVH/134eE94KuMJrvfXWa250oxuVNvMWIx1d/aFN8bz4QKQEeUJKvv71rxeyOvTs2sZE3cMLY1Ki7vp6JcvM5ApWb3jDGwoZ4a213E9eeCiRSeTFa2hqGVQHRJSX20TFBIkskvPVSso3ITBGyDKvnUlCX5+vVr3mVY52IUrG6pZbbll+Icev5Wi3SaMJpcmzVYshwrWS+iFj8uUpNHmgC1cLa+P87LPPLrrzFre4RQl16dO10TZYmXCYsFvBMLZ54ddV2Yh252cicEFCYNUIJVAZQEaHF4XHjxJGBi92sYsVYvjtb397pEeDAqJMGS7KGgG7+MUvXjxa8hRbRsm7BwnpShSiJRgeSF4wsV8UpOc8o54ORoDX9DnPeU7xgCLACCvPQJci9Kw81AEJ4LFQtzvd6U4lxsxzlH77cN4zvFuMA+LAAwWrZUowgRmvL8+zfmVUYaHNQwnesZzGk2uDAS8GotaF9VBeq31N3fWhCQICKHYSESZnkyYyRBbIGXkjC+973/tKSIiQDsvKPJ+IYo2L58iqGFSkU7iGMBLnVyvBwWTLOBTmINbOpGwlOKxWnacpB/6x89lYt5xrp7sVDGEsPJNihkeF3kxaB+XaIGP3PnkzebX6A+daJibNd9z7lYFAmhzTn2J8wzs6VL7xTZavf/3rl3hgeoKeHnpm3DrlfYlAIrD2EVhVQqm5jA7vnxkqonW9612veCgpGaRulPFx3eYNJA+hFGOHuMjHMuEoBaV8niMepBvc4AbNHe94x7JDEhFsKzb/I0gM+E1vetNCgHkxh4ieZxh2cVQbbLBBs/7665fYwfilDNe7jiAEntt7771XbalyliKoXfrHEqDlMJ5nnkoxhQhXX4rnGGJEEqFkkIdw7strtc+ru76znC/UAZlDBhnclaRYQuU5t+wvplfezjvId5s4qINrNsbwpvMKW3ZdiYd0JXWOZ9QDaTBG7nCHO5QVgze/+c3nI79x/7J/6ndETmiBvuKdpsOG+moWbabD6CReYOE+dlGP8hDOotw6D21ECE2e6FEb8hBndetLrpnk3Pe+9y3e96c85SnFFgw905dXnk8EEoHFQ2DVCSWCRqmIkxP/eI973KN4Gc20LYdTVH2JwaK0eAH8+oTlJcuLlrsRvnE8nMoPI2Ap2mYPSzF9RJayQ3QsYYkNs9TYd696MzI8awgBMolUWo7qClzXnki+u8fymTgsdZLXMiVtgI3YVZsSeJ9tzmFwTSK6kme0U7++8Y1vLF4e8YIrMZDyah9dZbbP1c8MXXNfOzlHZhFg8XJej9K3k739bPt/eSFk73//+4s829RhE5gJCjnsKl8ezvMAC/VQB0aeB56sj5M83z7q54au1ff5ri+Frtgooi529c57U1C7DqvxP0zIukmsVQUxwyZDJrvRV+6ZdZKnScUpp5xSJtMmX5PG6g7159C1ui3aaMla/5rUi/PlWR/SWfImp8cdd1xZ8RFnbBPbkD6ty8zviUAisNgIrDqhZDBtOLFMgjgI6ra0Z1MN4tXlKQQhZcRw834hk2a3CCXPJNLiXFdsYxt+Xi8vexZjZseseD2KsU8RUpyMhGUlRxiMdr5RR0bczkdeoktf+tJlSYryd14bJHlaslQuPOK875a9EbAgEV3lLOo57YAjMoxQxE5vGwb64qU8o0/0qyVDr8axTAqjcZM83K9sOMPR4bvzcQTOkW88x6C5n3zJI+6P/NTPEfnF8z7dK56XYeWNRaQZ1nZZ9TPt71EP5TG4NlrISziHzWqWN11zX1e+6mADjFcsCTXg1ezDuy67LlfbYRAe0BoD1wIDWLnWVQ95O88bbdl70003Le9FPOusswp2ddnL+r3GDF6WcG2cMnkUZhArEUMYTdN2+epbr9IiI3SSuGPnR6Wo+1Bfk/Ho65D3rr52zgQYOTRx4J1Frj07lOQpdEMoDA+ryZN8MiUCicDyI7DqhJInynKcwH0eFd8pF6TSMrbrXQqMwuTlsoMVCT3ttNNKgDfPAE+jHYRdXsB2F7nHvQgfI8AIM8YMZVe5zrlumcYvPjCWXfcpx3kK1TLfVa5ylbLLk6LlQUAI4h5G27Kg5W3ezCAL2o5kiz1DIvrKabdpkf7XTzxSYvl4Z+1KNnmwI79t9LQP7ki6NgvwR/bhM26SJwOJ1CEuYmvFrtkZ7vVLjC+5QfLaS8DKhjmPomfIFM+ePjaxQYwtI4tbdJ0H3euRGMXoG32n/ojwxhtvPBhj29Um+SAmvOZItdhZZIFc2yXM66VNYtSU494oO/JTB896W4DlRy+d1tZRyXMmNrzuNvPAzGQIbtrpmjK1W/sdljlhCfO+BDukmEff4TUx6r0uJLJpwkPWYIE4m5iaPBrryJJlb15aOLRlfloM9JmVGCEF9KZx1qczoyzyoh7qo6950/U1HUT29WfIO1JI3h2+639tbsucvEOPCbOwsdEGxlFyp/70Ic+qGHpvhDA+u/KP+udnIpAILAcCq0ooKQ0GHnFE5BhJr5MJcmeZGBlpKxf/I2qMnRm5pVGK0DsPL3e5yxVDTknVhr4PfoTSsrLXV4i9tFzjZ+NsoqDYKFaKMgimshEM5BexHCqD0paHmCbeOUuWyAGF7Zq85KuNXrsBg9obp1zGHKnqIg59beo7rzwKXJ0dSMC0R+SlHV3GUpm8bEi1uDKGVqxrV6iAujFAQhgsGwo/QKS78m23UTnugxMDa4MA8sLz7OXfXiptSc2GIP3rZetBVj0b9TzxxBNLOEOENHhe3RlTxo7hExtJRm1A4BUnB+ouwcMSv5ANBpIhJqvyHye5j1frgAMOKBtwhG5suOGGRTbJtjy9gka8MW+68IsaH8+rA9LJW+9e7RzyFHlGHuRafS2RO+CGqMhD2xFsfQIb5YtzdpBphLOvjeTYO0O9vgvJtnmETPTdPw5Oi3KPsfuYxzym4GQCZCJMj5g4IXheh2Zy4Z2r5MI4mWWSHx1B3rzhImK6h7DV10gn/amfjQ2hRuLHjTv9SWbEtetrMk/ebawybmp5r9uiLnQ4z6x+ls8ocqieJpfhTfcDADysQ/Wvy8zviUAisLgIrCqhpNgoWXFhZtbIJSLIiIq3o+gY11q5+E5xmfGLu6S0KCRLJYw/LyPFXhOzIbgZO8aX4rRpxKttrnWta5VlQt5SS86uK48RVLYDEQ0S0Ze/6zZQILraw3OK0GgT4uOgcClvy5KMjqXtaC981G8SQtJXF+fDkJx55pmFgCPhszp4VsU5KqNO2gIr3izGFsb6SZhDTXLc53/G0TKxvicbozCOspSrHK9OEe6A7FlCs9MaweQ98ks9ziO2DN4LXvCC8zaI6FMkF4my25/XRuwqryovH6+qg0wIxUASEDzvE9UW/aQN+tSz8uel4bkatw3RFp5AxICHhwEnj+QHQWbk1YF3nPey7Y0KvE14LHd7r+moHebqh8h7RswnWUQA4aat2gE3O8zlhxCahMEXcUI09Juyu1KMV+NcKIvXYpH7tqx0Pbvo58gcom1TH9JtQupNDl6FY/OZH0fQjzyVvOaj2gzD+hjVfmOG7lMu4o/Q6c++vnCeHtN/xqGVFh5nsYteK2XznEmLa3SiyTDvpVdP6WvE1eTKpKWdtA0ZtNKiPsZbTJ7b98b/6kNvmAB6hoNgtd9GEHXJz0QgEZgtAqtGKCkSSokyZsQZSIrOUp9ZMqVsyc5suFbCvvNa8egx+AgEz4pdhYy4pWUB4chpn1KtIaN8EbxjjjmmKDSEx7swkQWeIUaZkeWF4AlFcpAGeY/KX/soX54DearbIx7xiJIP402BU9jeI0iRI5vaUqdxyqnvH/oOO+SC0rZzfFYHz5/3ZiJt7aVP9Ue2eEO8SgVh11Y/Qcj7K7lHP/By2RHNUxIhC0PtiWueZ1gtLSKDYth4VHgWEReERlmIHyLIMKpLEEq4IFTIJ8JkAmF5W1+ZoJAFddJnJi/xRgAxjYw4shCEEsEThiF/EyMeRPWbJKlPTDbUGZG05M17Y3wow1jRZvfWyf8MtAmamDrvJdSW9n31M+puHPG8IhhkxDmHCR751FZkUKwyosmTa1zwvNvJPkQolW08Iljy8Ilo6PNlTyF7xi35t9xLXsIL73z0VV975UFG9afJhPsdvjtHj+gL391XyxO5RfBNOhB+E9/6eo2v8/LSp+ScHjARjHyFd5hMWLInv7yGZEHMN9kwFuLtGfJpJ/lrrxUA5FDst4m9Og8lpNx4F3vJFkSMbV87hvLKa4lAIrA4CKwqoWQ0vdRcrFEs1VCIZsZ2aiOW9TIRBUP5ie+yRONlzwiDg5Gi8ChW8VpBVkZBK08kiEFFGnliGG8eISTQi8iRTAaV10FsHy8AhTpK4WkfTwCCc+ELX7gsefOe8r5RnMiNGb+lcB4BGFCu80oMOwXvPZvI+CwP3gjezi5Dwxh6ubklQMuB4aVCjCSGlgGFleU1EwKxWkMkKDDSB+5DRnlGGDweSEuzsZHJPeqFjOk/xD5i+ciT8k0qkGKElLcG4fErR8gB4+jXk5AicsUjJJYRWbPszKMShh6ZE/fKG8t7iYCOkpNoS/3pGfJDJmxmISM2NkUsbV+esHAPTyaPuHECmyEsjZ9Yzhcjqr/k79PvkMebF0zwEEn9ZrzACzGxPD9EZJTNO8cDb3zCuB7XdbuHvqvTrI+h8ia5RofAznimh5D/Wv768tIe/Yz0C/cxCfIidDrMpIu3G7a8iDzS7RAQ8kYGTR6EJ5iAy7MrOY/wCTEyzujR6GvyK+7WJJP+Mz6EnsjfZIHs0ctIonHUR46NY15ZY1yIA50Am746qacxaAWDnCPFnAzODT3T1b48lwgkAouFwKoRSkaGIeNptEQZBoYhZ6wZcp69+oXelB7laqk7Xu9D8fCYiNuJXdSWkNuesiGYKS5EzmydF1Lelnh4J2MzDXLJu0ZxW7ITOzVkpOWpfTxu8kCQxStprzIclnksaSJallODmAzVdZpr6gQvGCNsszxiGb/LCARhZyyQc3jAGMlwP6MWsVcmF5Zbx+0/faDvkDzxaoy53eGMYxg9ZcjPUj+DiXAKQ3APmXIfLBBtS7z6jdG01EumeCd5P5FS96o3L496ek7eynC4xtNMThAw152fNHmGcbaBhbeIsR3apBb5w4NsIm+e4z0eFZOGcPJWIaHaLg/lIzq8U5b8kYzwOMFB3RCNIDxD/SW/IJTqBPvAM+o96hPuPHbqp32zOJAlbVlJ/7Tra1zxJtMZ+p4nV/5DeWsTDyOyiBTqKzJIJ9jNTyeY1MgLORPeY0LouUhw9UYBRJ3OQkz7ynTeOKUrjb+QC+fhQG+SeX2tbDrZebjrL31tshUkNOpQfwahhAN9R38P3e9Z2CGUJm7GsO9kr68ddXn5PRFIBBYXgVUjlAy5+EI7tA8//PCi6CgQBoNyFbsmlscsnZKKa5Z3KFfGnKJCJijyiOeiVC2ZyH9UkmcoLUYvDCUSYKnakrTyGEBeRsvwSCWiYHmoVuztsuTLO8UbaXcuEsPLxWvBiGgTT49lMvfwjoYxl5e8KVX3jtOWdvl9/6uXts76CBy7ylV/xslyGoJmmZQXEinQTphYfuY1EW8WXriuvNrn5I0QyQ9RJTfeZyrvSOpGTnjXvPjZfYw24xp4MMy85YgkA6r/eVks/1n6rkMoQlaQKN8jySsuhEfOAAAgAElEQVQIJXnhvVO3lST5KhMuvJ1Isnhe7RhKntN2hFL9vaIJvkP9Qw4RaRMqY0ByPxx4zJQPV8Qm+sZ197Yx6KqbOtWEkhxM+l5M44AHD6k2SZv2MBaNbZOrug+76j/OOZ4/P4eJMCPgJoy82UO4uy5cgn4x2UIMeYTJDE8jMo/80YMImomKvq3rG4RSuA+96Nm+Mp2HI72GINKfkvO+WyFQ//p9p6459LV7jFf/96UglGTvnve8Z/Haem7omSCUJk1CBkwOk1D2IZznE4HlQWDVCCVDZMZr6Vf8I4VM6VCy4mnM8h2W4hgxCg2JpGAtBzGCFCsFSZFTYLxTvCyIQq10u+BXFkVHAdb3Ou9/5VF0ymH8LKfyJPJUivWxvOWevkTxMtDiyyyfW/7ULnkqw+EeXh6bUMRSMuBxzXfLneLxRnk6+urQdT7ap42zPOTbl7QTSUdIkEn9ZLnUOfhrJ8+k5WgbY4ZwbZcBT8tqyBsvMDlgiJ2PpJ1h8C0DM76Ms74NvNWR7JFLRtqynUmAPhdaMYocKEteQSjDQzNk4KN+XZ/qg9wKS0CATZTIyihstDUIJY8Pj1dNhrvK8gx5k3f0o3PawqMOMxtvIs457unKq+tc5BVeU2N4kn5WnjEgdtXrxWAx7WHMRUyq+k2bENMIp0CKTEjIUhdWzulf8bXItTdMCJOQRxB01+k2k2V1pUeQ/tARUd8glPparGt4HeN6+1NbQ86jbj6NQ3Kury3bm8gYH5OmmlAa0xFXHWV15WesxpI3x4B46ySUXUjluURguRBYNUJJYVAillZsgvC/RKGapSMGll54hyzjOBAS3ifLLhQu5ch48miGd2pc468cs3SELYhFu6soQWVQeJbVeXuQQ8obue0z7p7THsYAKbYJxFJr26vpPucs+TNAYdC1zfK32DWvpWE0hhRyu959/8tDGciVg5Ge9oi8GJKof7t87TEpQNJ4/IQzMOZIhaB/wf+8GV4KzQM3SVsZWJ5HHjyE0i8l1cvd6sKLyeOD0NhoxeDHslq7rvob0dVfyK/8ELlxvMTqHYSSh5LHFUmYpD1RH+UhyupqHFiSbm9Qi3vrzxgT2orUCh8Z5aGsn4/v+sw4E16CZCDssXwZ94z7qU5wUSfjwTLupEvexitZ4b3Sd9MeXo8krpf8rqR/6rZ7HvkXGiHWWqzh0Fsm3E8/hMcc8YJ1W8bghiBa/pYnokUX1fUNQklf2gi0kgmMvvYc/Wp82IAjjjF0ct3WUd+DUMaEynL+uB7KXPIehW5eTwSWC4FVIZQUIkVoVi6gn6Gk1CSflkd5AWsiJlCdcWQIwltE4boX0UNUPMPQ9BGb6IooHxG1QQAprZV03BefrjE8AtPVibfAzFs5XUm9ECgxTwwMYsIjR2nXSb6ULQ8S5e1/h/ZZ6megbH4YpZDrPIe+qxeyZAewpcNZHfLjJUa620ZRfZTL28KzbOkUKbehhSeRd5qxFJdq0uDeSRI54t3lSeT55PERZ1vnE5MU3nDlm8Qgs4yzeoWRhn3In2VLy/M8JuNurPG8+kQMpXgweDs/SXK/Ots0oV1kexxPozK0mzyTPeSNR2+U10p57UP5Jk02esDMGINrjFPlkEvjwr1DbXQvXMJD6f2ysbFtXFzkb7zpK2NlFof613Iybl3a98lDmI2laWN9nKVnEzmrKYiXJW/eSjjKq+4L9xkbNh0KEWqPL7hGDKUJzCh5q/OOPpOn+nuevrLTv96d7T5109cxVtoYxP/Gk4mjdhnjQ57aeEae9LYlb28OoBdCH8Y9+ZkIJALLh8CqEUrGiSIVQ1nHDlKoZut2WzPolraQK7uILXlZzmbUKDmKUAwSLxKjizDYeBFGrw9+ZSCxiAxPWcSF9d0vP14eS1oUrpc6IyR95chf+8z0ec0oV68lovzbSTvk4xmJ4uaJ4bVAjpDQuNZ+dtL/lWMzid3zPHqzOuxcF4AvqJ+Rbif153m05M8DYmMOw8H7itzxQvMCavskCXbIkxhcsV+81MgteXJN8qls79KLHfXiVckRbHm062U59ee54mEUSxYTjnHqpSwEwHIhr54wB3IQdRknj6gzg8rI2mzB+4R8MOijEqzJM/KmDsbEqDhBcgEjh+fVV/kwY+T1mfFXjxM48TJqaxfRqesZdUJyjQUeNyS9b/zUzy76d1jRQ7y32ibeVDwvrPpSyIk+FVZh8mKDnlUJz5lQytN9+oSOEx6ErDlXJ/JmYmzygPyPmvzAXN8qI/BH6Ixdkxfe8AMPPLBMckMWQid5HZCJgLp1JXWz2mOSyNtoEkKX9d0feagPby2HgOV9k+hRToF4Nj8TgURgcRGYO6GkdCgYy5IUoMD4Oi7IdbNsS328gYglcmWHaf0KCvdRhALMKWVeFDsXx1lipKwYQ7/yIe4IWaQ8+5JyxExagkZc4hUyfc/IH3HjsbCRxys4LNVR4n1Jeyh4RIfi9hohQfIMimuzSGHYEQV52+Qxi0NeNkwgaGGk6vqqP6PBu8zoikPVXwypVyfxPveFHdT5dH1HHhEVBBCBklfIU2DKSPOaMPZeK8Qzq88ZakYPBvoYPgy068gkMiW/oX6r66Q89yKC6uMtBQxqFyb1c+3v6oE8il9UZ+/ttDlmnHqog2VH79RERhloRKWrDu4lq0ivV8QYS7yZCASvOXIehKeOnww5Mg5448j6EGlQNu8zIokc21Ajf/kse4KhMRqbl+gr3np90Jc8Yzx4r6nVDp51ryQzKbMKYgLNY0iXIZFk3Pjo6kMyYTMZXG0WRO7dp4x20tcIp/GqDHpWv8k/xgdSiRAqT/84wuuuryM0p523/5WpX+Odvl5kX68+dT3jnDbaxGS8KSPa0Hd/nk8EEoHlQGDuhJKCokC8DoWxM/NmwEIB+mTsBe5bPrJDmqJk7BhZ1x3yoQgtd/IaInp+sxghiLy6IHcN4WBAGX3EjfLu8qy5l8KlFBlBO4R5TIeWcTxDyTMwDDpCqZ2Wgyn0um6+x6F8xhwR0F4xfPWyU1dbVnIObspCoGZ56EN5dyVtVBYvSPzEpfd78qrwLlru6zKWXXm1z4UxQt7IAEOpf9XFoS94HE1eGG7L0GQPwUEcxZ2JdVX/ID7iBXlREd54tVC73L7/5eMZxtHzQ7LSl4d6IBMMMq8rj3hMpvqeifOw1r8Ivs1HwiZ47dWrneCDAIjhJdsmPjy2CASSaAkUZrzJPEjhwZSX65ZGeUJHhSq43wQOeee5Euqy0glEuw1r+38Ywstk1ioJb70+J+9DyXPkngdcP1mNEQqiv22AgrnVE5MT+feND9gKTeDds+JQe9vb5fPm8zQbK1Z1TBL0g82D3jRB35q8GB/GjTpG39HHxmr7tUV1GeroOlKsn2EyTjiRSTQSCgdhTaPkqS4zvycCicDiIjBXQklJUR4UoJmoV2UglIghIxjkCnEUh8Y7gyR4xYvZNOXmMPunZG3eESPGk8kg2mCDlDF8fQpYGQgrI2p5h+EXg0S58xogkJ51MArKPe644wohEU+njHrpL7oSWURk1N0Mm2JkjC15qyMvkWsUOBIUBwKszgyBpSvL0ZRxKHtKfR4psJ7F56j6KQM+SAhvs/5yMGLiBOG+0kQehB/AmKzwVjJI5ImcMO6ID3JvM5AybQKzdMfw2YgQHjx9j/gjvV4PhRzw6EzSB2FUearIljAN9YDBuEkePJtIhTrbsMSL6Pw4STuMMR7y9q8S1c/Lj9wLzUCgeXgtYTtnRzHvGZKEhAtXILPGFoxgZzIWO+qH2qd/EVwTK544ectnXUhkg44wYUEK6bRxJoLwIqPu5Z1D3uHN08grTHcglyY3MIZXF8b6kCyTN0TUhCA89DW+6kmWyb9JuryROHXn4SQr+hrRjMm7MoUf0b/aRQa68o5yjHF6zGRYfeo3KcQ97U/1Ik9ihHlHebFHkdB2Hvl/IpAILCYCcyOUlCEvh/gcSpLSRAAoH0HnvCjhwaPILMtSchRgeHkYJqSE0vQMUioPXkCEQUwmg2hW32fEKWBKkhfQsibyhsTZacyD5hpCQtHyCpmVMxa8RJYgeSvVs07yZBh4xyhRnhj1iZeh8/zwhvkNZnk4vG+RN1I7eCnkry5IruV+ihkx6DIiddnL8F0btMVOWJuwGC5tZdBMJqZpI4OEpMOW5wUZZBCRTEvsSI/+8/ody91ImuU9HmcxtGQpZIWBF8spFEI/xLtBJ6mf+pg02XhCNsfdTBP9GFgZDzBi/BFieY5bD3UwqTE+tJlsd+HsPp5QoRzIEJm1DGuZn2cT+XcYh+KHjQdk3GQsJldd+UZbfKozIkrekUnkWB4mAutCMvYRImQQKbeCAVPY9iWYuB6fJtomDGTWBItOCP2E5AstMIntytM5OomMCCfh1UbI2vcqy8TV2wKs6Fgd0M+8kdHXJmXaIbzIZF1cKI+muhgzQ2EK8jcBJz/GoVCWIMJ9ODgPP5Mf3lV60mTDeMyUCCQCy4/AXAkl75HfN44lF7+37H+G3RIxhUQxIWyWDSlJs+dYxg7DxMiZMdv0gEDIx4EYmunznvBCyqudEBuv6qEk/TKFncfedUkBIq+WQMVVUq52KfLOUMJIqhjBIL11vs4hozbRUM7aZ7aNEPBSMaTOI41xqCdijfC4n3eB8dcepJjy7qp/Xe4yfWfgtAnhhgeCgoQzKNMkGMkD4RKPqx/JBpx915fIOTJrqRVZRyr1B5LP+Ecd9CPZ0C/u4z1HMidJ6oMgkFsGnvdvknZ6XpmW6T3vQCgi3GOcusgD0UOyLSPGK4einZGH+0zeeJ7U0xgij77bKGWZ2utjjEOYOQ9XYwNpH1qKjTL0u7FoadxYMEEzWWsTnrh/2T4RY5MQY53XjzcPLrBtJ+f0gevkMbx9zsPDIT9953U7MSnxqrRaTut8PYuAmZySFYTdpJieayd9TY/xFOtrkwJ9amldX5N3k/K6r+lDHlRjV9272qUcdSejxiAsTJLJfVc9ol7yovM5GjwjhpS3dV2ZbEQ78zMRuKAiMDdCCVAzaYqL0Ra3Q3k4fK9nv5QTo+x+JDIMIYNvlm050HPyiSPy8j+F2kcE5OU6cusZypjBo8ARDq/38Z48ytxyqJduU4yUcdSjLRwUI9LLK6R8bRSXiRQ7fI/6tT/dW9/PS8HDMKSI2+Uvw/8w0qc2wvAOjhNnNkm79A1PpeVaeNspSk4QTQYKnhGO4Jp+YLjrPiV3+lkfkEnf+wxoX93cL091YLRNFoZ+4cb9ylU/h+9knveIx5DXBrnsk+euesiToUYGhYIgieJEefjb7fF/jDV15pmCDdyMN88gM/DgoSSrxqVn2nm16+K6etvQhpiYNPF0ad+oZ9t5LcL/6tzuK6TQJhfx2Npn811MjNt19jx9wysOD3jLr52cQzqFwCBaYhH7Nrx5ltzQX2J2kUGewa7+UT6ZtgKjH/W1Tzo1+lo55F9fq59lcm3sqmddb8+TEeRUPKc3OBhfQ8+5RrZ4QZFhJJQuHnqmLjO/JwKJwGIjMFdCSfEx7l1HbdhDcVMsDv9LPt1HeXXl4Zxryoln2nA7T+FbQnK//NzPcFKAFCoPioNyo0zl6bm+PJWhnkP16qtv+7w81GmorHabluV/GMEZsUM0ZtnG6B9l6M8ax7jmM+SnT0bc41rf9XGwVgdyZMmc15m3j9F2vk5RFlkUysGAI1uMrImNsBDeJMuh7WfrfLq+q3+8oxXRMYHqWgr1rHoEbuTRs/53vr4G0/paV7n1OXkgRkikpVRL8LzFyli2BAeyoz1ImImiSSTibUXEq5V22mmnsvzd11fOmzjrWxMNWHSNdefka+UEbkJDlKsOXcl5k2RL1cIT1MfzXfWo+3OWfY0o88qLneTFNpnpI9bRBuUHERa7afl9JZO4yC8/E4FEYLEQmCuhXKymdteGwq2P7rvybCLQjwD5YUzFCgqZEMMpNq5tYBl8XlXhHmLvLFfynvL08EzaoGEJ1URn0qQOjLzlbPFplql5pGZN5PvqpXyEgbdT3Ox2221Xwh2GiFFfXotwPvDkbdZPwmH0KS+4PhbrGO9v7KsvosiDLmaVN5HXmCfRebLgQNjJBE++UAWhGeISRxEt+fCOCvsxCYkVAPWed1J/hFasrnZ5CXsfoY26qJeJ1LHHHlveDyvGmee7iwTHM/mZCCQCy4XABZ5QLld3ZW0XFQGGkVfSb8Bb+rasx2AyvpGQBwTDxjS7ucUY8vL4CUrLhuI4EYNJlrsjb5/yt2HEzlkeIKEGDH1dh/r+WX7Xfh48sX1inb31oO/1RbMsd155wcyqhU1WNpVZovXGByROXyHMQhNMGvpSTDLEb1siR8B46HgtkX9E0nK0MBsbn5BJr0IbRc6Up37ky/K4usTbDsjAPBNiiOzaRGkShGzbzDOEg2d4u01wEGByDkf5ZEoEEoF1B4EklOtOX2ZL1iICjCYiyOOIMNogFK9jCS8Mo2pzBm+X1yghnt6PytMl9tEvqfTt7h2naerAc8UTpgyeMWQVeXFtXkneCIXNKt5fyENr9y7CNM9y59Ue+SJsvHA2rXhfozcBeJ2YX8XxC0xIex0H3lUX4QyW/72OysYvXmlxg976YOOL2Eqb1sRwi7H0gnHhDuOQQriSNxtrkDobDHlTYU7e5oV7yLDwDu1S51Gxk7A02RKjTubJOeycz5QIJALrDgJJKNedvsyWrGUEGHIGnfeGgUcsxZYFoQvDalkbKbF0iqDw9IiDi/djTtMMdUBKkRVvU0DwvKala4PONOXEs4gLkqHu3u+KTHoNTt8u5Xhu0T/hiPTw8lrWFUbAi6jPxETakIf4DRE3/a4f7HjnzTR5QKb+X3t3AjfhVP////H4fdu0aEXRHilfKomKIiVriwqlLC3IkuzSIlTKFqFUWqQkIVGIoogWbWhBad/30r5f/8fzPL7Hfxozc8/MPTP33HO/r8fjuu9Zrussr3PmOu/rcz6fcxGjBJn0tLuofoE1NTivV5qt3BzHKszKTawSpgRmP0E1ren081pemPAVZkVlZWQdreuldiuzcwhOAhpD/sX9sOunTDkmBEJgughEUE5Xe6Q0i5iAQZXIYI2xwDkLoeWSTPXxL/Q9S56IWk98MiATG5YzElDjmFFsBJ6pddYvS7NYhoafps9HvREMgtlYqljZTL2KGh5HXqMue6/0tBULoMApAkpbqRuxbGWHftwS9AUCTH8g6FmPiVTim6+pYB8uCoSk6V8sB93kYWoef1ZprhYCiEbVl2p58GBxNe3PIkoICyzrxcE5xK3gG31DRHi9uanp5n8IhMDsEIignJ22TE2mgIBBlJiy/IopUtYs07/EhM33BnuWTL5yxIbXc1m7BqlazcP0uilXQTIsYETNqDflJowEWRBbXhPNyrDYt9qWhBTRrK2sWKB9+60fkWh3fN1ZqnGze91vWp14Old5iFQ3Mdq6BvV0On7Yz9RBn2bxZmV1UzSX1ds5LNWssQcddFDxtfQ7mE99hy1/zguBEBg/gQjK8TNODkuMQB3kiRBBNqxc7RYjx7SKjVEjkr48DeimQYlLwmPUW53GF2TBKspiNWuCQX1qe01j3ZRJ2xK8VhpgIW7vb/Ntd33V1LWF0llV5xKT8lMuAlzfUKa5ItfnW8acHwIhsLAEIigXln9yn2ECBmEDu8F+oYRILcMoLaDtTUZUEpL+Z1s4AuNua+mzPg9iVXWOvjHO/rdwxJNzCIRAK4EIylYaeR0CIRACIRACIRACITAwgQjKgZHlhBAIgRAIgRAIgRAIgVYCEZStNPI6BEIgBEIgBEIgBEJgYAIRlAMjywkhEAIhEAIhEAIhEAKtBCIoW2nkdQiEQAiEQAiEQAiEwMAEIigHRpYTQiAEQiAEQiAEQiAEWglEULbSyOsQCIEQCIEQCIEQCIGBCURQDowsJ4RACIRACIRACIRACLQSiKBspZHXIRACIRACIRACIRACAxOIoBwYWU4IgRAIgRAIgRAIgRBoJRBB2Uojr0MgBEIgBEIgBEIgBAYmEEE5MLKcEAIhEAIhEAIhEAIh0EoggrKVRl6HQAiEQAiEQAiEQAgMTCCCcmBkOSEEZofAf/7zn+bf//53849//KPxetitpvPPf/6zpDeftIYtQ84LgRAIgRBYOAIRlAvHPjmHwIISIPr+/ve/Nz/5yU+a6667rvnb3/42dHn+9a9/Nb/+9a+bG264ofnNb37TeJ8tBEIgBEJg6RCIoFw6bb3ka1qtaCxyBE+n3XeO62Zh65XGXOdOSwPU+v31r39trr766mavvfZqXve61xUhOGwZCdOrrrqq2WeffZoTTzyx+fGPf9ywVnbjOGw+OS8EQiAEQmA6CURQTme7pFRjIEDgsJ4ROz/84Q9vsf/oRz8q37G0dbKwEUeE0y9/+cvGsa1pzHXuGKozdJK1HsTks5/97ObJT35yc/755zd//vOfh04T2+985zvNYYcdVtI77rjjiuWzE8ehM8mJIRACIRACU0sggnJqmyYFGyUBIopQPOmkk5rnPve5zRZbbNFstNFGzROf+MTmSU96UhFBT3va05rtttuuIYZ+//vf3yJ7FkjTw69//esbxzrPLp3NN9+8pHv00Uc3v/vd725x7jR9wF/yxhtvLOV91KMe1ZxxxhmlzOo37IYvi+c111zT7L///oXLySefXAT8fNIdtjw5LwRCIARCYLIEIignyzu5LRABguemm25qPvKRjzSvfvWrm0033bS5xz3u0dz61rdubnvb2zb3vOc9i7XuiCOOaD75yU82f/nLX25RUmn86le/at7xjncUIXrnO9+5WWaZZZrll1++WXfddYt17pJLLul47i0SW6AP1IGFlSheY401mpe//OXNT3/60xJIM98iSfuPf/xjc8EFFxTB/ZznPKe5/PLLi9Ccb9o5PwRCIARCYLoJRFBOd/ssqdIRJKZIWbrmG3XcCZy0//CHPxQBddlllxURSFDe6U53arbZZpviT0gwmvrtZlVTLoLsoosuatZaa63mgQ98YBFlLHM+73VupzJN8jN1Enhz5ZVXNo9//OObddZZp1gU1WlUG8Y///nPmyOPPLLwec1rXtP84Ac/6OhCMKo8k04IhEAIhMDCE4igXPg2SAn+jwDBY6qZ2OPfN5+o415Q+fsRORtvvHFzm9vcplgqDznkkDI9S9T22moZTz311Ga99dZrXvnKVzbf//73SwBKr/Om4bta7+233755wAMe0Bx77LHFajtXnQctu3a74oorGhbKxzzmMc373//+YrkcdT6DlivHh0AIhEAIjI9ABOX42CblAQhU6+TXv/71ZsMNN2x22223Mr08QBJ9H8qKJojmKU95SnO7292uWCgFp5j67SV6fMd6KpqZRXPrrbcuwpdQ63Ve3wUb84Gmoz/84Q83j370o5tHPvKRzZe+9KViORx12YnuX/ziF83hhx/ePPShD23222+/4rOJU7YQCIEQCIHZJBBBOZvtuuhqRdQQHCyTj3jEI5qddtqpTCGPoyJ1WlYed7nLXZrb3/72RcSK2u4mrnzuPNZIQSebbbZZc+GFFzZ/+tOfxlHEkaep/KLbX/CCF5RpeiJPgNE4NnnxQRU5LmBp7bXXLkJ2PlHk4yhn0gyBEAiBEBgdgQjK0bFMSvMgQIRUQbnmmms2u+yyy9gEJQua5YNMc9/rXvcqQTksaZa9UY5OW53qPvPMM0uEuHUbWTmJzEE3eUjP3i2/9jR7ndP+XXua3vOTZJG0RNCDH/zgEjgzToGHy3e/+91m1113bVZZZZWyNiX/1Paytdcz70MgBEIgBBYngQjKxdluM1dqQoOg/MpXvtJMQlCa/n3ve99bxA4/SqLn+uuv7xiMo2ymur/4xS82L3rRi5qtttqq+fSnP13WpOxXIDmOyGK5s6yQwBUWQ8KWz6HvrHHZHoxUz2MJNY1s52fqWIIUM4FGPjdl3xpUVMvmv2Pe9a53NQ9/+MOL7yfOzu13q+XAQVnws3ut/DWvmp73lmk6+OCDi4B1g8CdYRgBXtPM/xAIgRAIgeklEEE5vW2zpEpGgExKUMqLCPrEJz5Rls6xbJBo7S984Qu3EFmOJYJYI0Uub7nlls1pp53WVwBPbUDCT37S+OhHP9pYn/FNb3pTY81Kr/lkChL6zGc+U0QXsVg3edeock+gsUbmO9/5zubaa68twvRb3/pW8773va85/vjjS/kE2sjDOVW8yd/7fffdtwTj7LnnniU/n8+11foTj8p46aWXNu95z3uad7/73eW/gBvl7rTMEhF7yimnFJ9NwTkf//jHixCeK898HwIhEAIhsPgIRFAuvjabyRJPWlASr/w1LexNUN73vvcta1SywClL3Ygu61eed955xTJ54IEHFktmP0vtSMf5rHjyIiL5Xj796U9vpCNCfIcddmh23nnn8lqgj2V2WC2dayfk+CKyjNoFArHg7rHHHs2HPvSh5rWvfW0p1957713Eruht0evW2zSlLQ115fspuls93/KWtxQx6rtem+8JYZbUc845p7gIsM4ShwKatt122+aFL3xhEY3EY/uGpWhv9X3Qgx7UcBcgPOfKtz2dvA+BEAiBEJh+AhGU099GS6KEVfhMYsobUELvm9/8ZrGemfJeYYUVip8f8dgqeAiqr371q+UZ1ZbBufjii28WanM1jDyIus9//vPlfMEpntDDusdfswo1Ue3yv/vd794885nPLMEylce3v/3t5hWveEXzkpe8pPnYxz5WrJB8IC3EvskmmxSRaAkjC4h7yo8gI6KR0Pztb39b6qIO/CcJO4KTEG0Xzu11kT/xxxL6xje+sVl//fVLxDZRalF04hALi8ArYyeBTcj6TiCQfNW7nW97vnkfAiEQAiGwOAlEUC7Odpu5UhMwREm7oGwVd6OstHQFjVhLkoXSU2/22muv4oPoO3udbvZkHELvhBNO6CwZOwYAACAASURBVPupMs43df2Nb3yjCMKHPOQhxbLIOmjqm9iy/+xnPytP2CEm73rXuxaByL/S+cSoSPLnP//55ek8rIxvfvObi7/nHe5whxIN77GJ/CZZQFkmCU1LAnnEZBVv0iH+PCaSsCNMOwnAylfeVUjzgeR36bzdd9+9+I7y12Q5JTgJU/VwTvuGH79OgTlEril+U++EdrYQCIEQCIHZIhBBOVvtObW1ITgIiW478UGAsaTVZYMIq27H1887CZl+IDjPMkGeyU2ceYQiC56ldGpZTVXzs/TsbxZCYrfVv7FXPsonKIUYJfAIvZe+9KXNddddd3MajiH6WO5Em/PjPOaYY0rQje8E3zhfQAvfReLMYyNXXHHF8rhHr31Whdtb3/rW4ifJn1IADNGoLuphCnyDDTYoeRCXvQSlvLEnoIlJj5YkavmYEqe+l27du3FwHPFJiK600krNAQccUPwwlTdbCIRACITAbBGIoJyt9pzK2hAQIpsJOFbBTrspYAEmfPVWXXXV4hf45S9/ueOx9fzvfe97RQASecTNIJvjiSZWSdZBj2B83OMed3OwCsFlurb6KJ599tklUrqffBzDwve5z32uLIDumeFE5emnn14sezUNgovFThnudre7FX9OTwlyru9MWYvMJjLxwW/HHXdsll122RI5XY+VHgbqYxqfyJRGzYeg5AP6hCc8oQQhmaZmVey0OYflUQCN552z3K688spFXCoDgcgiSiwrXy9x2CooiWCCWtv1OqdTmfJZCIRACITA9BOIoJz+NlrUJSRQWNr47Qkq8USaTruAFAEfpmWJmPvf//4lyKTTsfUzPo1ECuFHvAyyKRdBxJrHevY///M/RdARTcSWMptOFnzCEmi6uV8hVMUg66JgFFPZglc877tVyEmPwPLkGourP/GJTyx18bnyEYXqRmizZDr/qU99avGTNAXv89Yyydf7dhZVUPLVFITE37K1HK3catlFlN/73vcuQpt/pwCcl7/85WU/6KCDyjT+UUcdVYRl6/mtr6XVaqF82cteFgtlK6C8DoEQCIEZIhBBOUONOY1VIYz421nKhi+doI5u+/Oe97xm8803LwKMoCQwux0rOpq1TrQ0S2W7iOqHhXKde+65RfQRlKZ3+SL63PQuAUwICqrhK9jvRqwRggQUMWnK2GMIiSs86lan+IlOwTSe3FOn+R3jWGnZRVELhCE+TZ+zWrIStqZX023/XwUlwep8a2h2E5Q+J3KVXZk8mlL5nCugqHXXXqyh3bZ2QekJQ4MI827p5vMQCIEQCIHpIxBBOX1tMnMlYjUjfm688cbmhhtu6LhbVJzfH4EngIWY/OxnP9vx2NY0iMm5Ipa7AXUesegpOQTl6quvXqx3NfiFeGJlNF3fj3Cr+RCK/C35HbK2qg8/yfaldVgdrSHJQnq/+92v5OWY9ry8N81smSHBLSyHprD7rXcVlP1MeZvqZwklppX9Pve5T1kuyPI/XBDqrn6i33sJ7VZBacrb+pcswK1W1cos/0MgBEIgBBY3gQjKxd1+i6L0BBFxQUh021nGTPG2BuWIgPZ5t3N8Lt12AdYvFMJPkAwhSVCyioqO5i9pethaj8MIID6IorMf+9jHFlHG6spvsVV8KbMAIEvqEG7KYIFzDIhKAlz9KjuR4aylfC091Yf/ZDcrY3v9q6AUlMPaKCin27mYEI2svyyUa6yxRmPxci4AxKbz6l7L155ffd8qKIlmC6vHQlnp5H8IhEAIzBaBCMrZas9FWxvCiVBpXzZonBWSH8Eoqvz//b//V6Kn99lnnxLRbQHxK6+8sgi8QctAULK0WoCcADSVzQIrP1sViXwgBeuIMLdYuLKwhhK0b3jDG25e4JyQMxXvKT3LLbdc86xnPav52te+1vc0v8hsLgcEJdHca9kgeVl7UplN1fMhJV5bxXC/PAhOU+K77bZbydeSRxZtJzSzhUAIhEAIzBaBCMrZas9FW5uFEJQED4uZ6O5b3epWjQXOCTYC84gjjujbR7EdOvHFQrnuuuuW9ERxe2yh/GwEFavhBRdcUCKo+VlaGshC5yymRC0fRlZKx/LpZCVca621yvS49Rz5WmLWz8bqaHpaIA9BedZZZ3V9Yo0yspx6Yo9gHCwEJylDe37et3/WWh7iVNQ5a6d1LD2GsdOUfus5eR0CIRACIbA4CURQLs52m7lSEyaTtlBWC5pocVHWpr1N8wo2IYSqRXFQ2AQcKx+RaKq3Tp1Lr4pJ09ssjne84x2LZVRUtSn+iy66qAS+1IXJHU9YeloOscunkQWTIO13U09LDnmSTn30Yn2KTnsa2oHo87xuvp/KL5pbQJF0qoj0mnA2Fe51p833Isqt9enpPv08oadTOvksBEIgBEJg+glEUE5/Gy2JEi6EoCTWTMFaa9LUtIhmU9Cmq00TK9MwWxWBJ598cvFB5ENpytmak4Jr+FO++MUvLtPcrJNEnjUqWSePPfbY8nxvfowsfMQai6GF1R3Lp/GSSy4ZaCpeeQhCQTHyMgUtmKmTEFRn+SqLqOzVVlutIbhZU6VByBKRAqxYYT/4wQ92tToSpoKOWIBNt6u3tLOFQAiEQAjMHoEIytlr00VZo4UQlPKs1jiWOPt+++1X/P6IsGE36QquIQqJuHXWWaeIONPWpn3rOprHHXdc84xnPKNMezvOM7M9rYf/JOFZmRB/rJwiwQXmeJzjINbTWk+LpFsaicCrgrVTHR1PUFteSLn4d1q+SaS6J+5YvsjanMoq8pwF1Tmtm/fqcMghh5QoeunwI+0kYlvPy+sQCIEQCIHFSSCCcnG228yVmgBpnfLeeeedi0VvnBWVp+lpT7R5/OMfXyyDgl1GYUWTNmueqW0CkaCyCDv/SNPXxBo/SMsW+cz0ONHId1NwThWMBBjL4Nvf/vbyveWCWAgHEbzKok5E5CabbFKeREQY9po2l77vTd2zmiq7nT+o8lr/k0Dlg1rLWtuqtqXlnQhg090eC9lJeNZz8j8EQiAEQmBxE4igXNztNzOlryJENLPoaFHGpojHvRFOfBetN0nkzWequ72s6sRSSUgRXqK6LRquXj4nFv0nLH3Hb5NVr1WgVTFIVLJUDioma5mkUyOuCTyCsD63vB7T/r/mLfLcsRZrV0aCV5lZdztZHJ0niMd0OGvo+uuvXyLLRb9nC4EQCIEQmE0CEZSz2a6LrlZECHFicXNPZdl7772LEJtERQg44oi4G8Ty10/Z1KvWTT7qWD+r/+XpO7vP2jefOca58ykfkSfCW/S5NTI9DahbnrUMrWWUf62DcnQqq/McJ6rdoxo9l13EOhHq82whEAIhEAKzSSCCcjbbdVHWikhhiRNhLNqZtTDb6AgQg6K9TVl7OlBd63I+IrW9dESmqXJrXfKxNMVuen2Ult/2PPM+BEIgBEJg4QlEUC58G6QE/0eAGGHFYkkzPdrNAhZgwxHA01I+osT5jIq89nhLn41qq9bJww47rFl77bUb//N0nFHRTTohEAIhML0EIiint21SshAYOQGikp+mp9YQfIKf+EbONfXdT0FYOvl4nnPOOWUtTY+VJFhzc9APvRwTAiEQAoubQATl4m6/lD4EBiYg4pvV8FWvelUJgDr00EMbzwqfj48joWqq+4orrihrbG6zzTZlPc+bbropluaBWygnhEAIhMDiIxBBufjaLCUOgXkRIP4EIFkiSfDTxhtv3JxwwgnFujhswkSqxzvyz/SkodNOO61Egs9HpA5blpwXAiEQAiEweQIRlJNnnhxDYMEJVH9Ki6Qff/zxRQgKiBpmk5Zp7UsvvbQIVGtlWlbINHq2EAiBEAiBpUEggnJptHNqGQK3IMDnkWWRkORHaZH3YTeWSM8Ht86maW7vCc1sIRACIRACS4NABOXSaOfUMgS6EiD8RiX+RpVO18LmixAIgRAIgakkEEE5lc2SQoVACIRACIRACITA4iEQQbl42iolDYEQCIEQCIEQCIGpJBBBOZXNkkKFQAiEQAiEQAiEwOIhEEG5eNoqJQ2BEAiBEAiBEAiBqSQQQTmVzZJChUAIhEAIhEAIhMDiIRBBuXjaKiUNgRAIgRAIgRAIgakkEEE5lc2SQoVACIRACIRACITA4iEQQbl42iolDYEQCIEQCIEQCIGpJBBBOZXNkkKFQAiEQAiEQAiEwOIhEEG5eNoqJQ2BEAiBEAiBEAiBqSQQQTmVzZJChUAIhEAIhEAIhMDiIRBBuXjaKiUNgRAIgRAIgRAIgakkEEE5lc2SQoVACIRACIRACITA4iEQQbl42iolDYEQCIEQCIGuBP7zn/809n/961/NP//5z/L/3//+d2P3ebYQGCeBCMpx0k3aIRACIRACITABAgTj3/72t+ZnP/tZc+ONNzbXXXddc8MNNzTf+c53ymd//etfi7CcQFGSxRIlEEG5RBs+1Q6BEAiBEJgNAiyQv/vd75rLL7+82WGHHZonP/nJzSabbNI84QlPaNZaa61mu+22ay655JLmV7/6VbFczkatU4tpIxBBOW0tkvKEQAiEQAiEQJ8EWCZvuumm5owzzmi23HLL5jnPeU5z9tlnN1dffXX57HGPe1yzwgorNI95zGOaE088sVgrM/3dJ9wcNhCBCMqBcOXgEAiBEAiBEJgeAnwlv/GNbzQ77rhjs9lmmzUXXnhh89vf/rZMf//hD38oVksWy+WWW67ZcMMNmwsuuCA+ldPTfDNVkgjKmWrOVCYEQiAEQmApEeAb+dGPfrRZf/31m1VWWaU5/fTTi8WSFdL++9//vjn11FOb+9znPkVUvupVryrT3qbJs4XAKAlEUI6SZtIKgRAIgRAIgQkSEIhz8cUXF59JgvK4444rvpJVUNbv73//+zfLLrtss88++0RQTrB9llJWEZRLqbVT16EJuDj//e9/b/7yl780//jHP7IEx9Akc2IILG0Cpqj//Oc/lylpy/u4tsxnk56I7iOOOKKIxSuuuKKkXwWlvM4999zmfve7X3P3u9+9OeiggyIo5wM853YlEEHZFU2+CIGmXOxNDZk2cqH+wAc+UPyV3PVnC4EQCIFBCBCQ3/72t5v3v//9zUUXXVQCZOZ7g1pvdn/96183v/jFL4qYrOtO+v/973+/OfDAA5sVV1yxeeQjH9m8733vK+tTzlfIDlLvHLs0CERQLo12Ti2HJOCCbDkOPkrbbrtts9FGG5W7/T/96U9DppjTQiAElioB1sTPf/7zZWmfZz3rWUXc/fSnPy0Ww/kKvGqRrOn4LyhHxPeaa67ZrLzyys2+++5b1qisgnOptkPqPR4CEZTj4ZpUZ4CAiy7L5Hnnndc87WlPax784Ac3u+++e/O1r32tTHvPQBVThRAIgQkSIPJ+/vOfN+95z3uaLbbYouyslT4jNke1ycdU92c+85lyI7zaaqs1O++8c4n4djNcReeo8ks6IYBABGX6QQh0IEBMuvB+6lOfap797GeXu3vLclx55ZXFjzIX5A7Q8lEIhMCcBExxm4Y+9thjy9qQhOWZZ57ZmLIehU9lFZNXXXVVs9NOO5U8DjjggObTn/50if6WR7YQGAeBCMpxUE2ai5qACzIfSZZIU0QPechDmq233ro8acIUUsTkom7eFD4EFpSA64cAv69//evNfvvt16y66qrNpptuWtaHdH1xMzvsVsUky+QLXvCCZr311itR3/Iy22L/4x//mGvYsIBzXk8CEZQ98eTLpUbABdkdvOfhnnTSScWJfd11121OOeWUYkGYz8V+qbFMfUMgBDoTcJ2xYsTnPve58ljEBzzgAc0LX/jC5ktf+lK5mfX9oJtzzKoQkyyTnpDzrne9q/nBD35Qpr9/+MMfNu9973ubs846KwubDwo3x/dFIIKyL0w5aKkQIBhZCQThuLu31AYrgsjMUfo4LRWeqedsEyBi/GaGEUCzTWbu2mHmkYkEnmdu83O0hqQgnUGnvquYvOyyy4plUsDPu9/97hKAI/Kbj6ZneXum9xve8IYsGzR38+SIIQhEUA4BLafMJgEXZVNR1157bfP85z+/ufe9791stdVWzWc/+9nG0ygyaM5mu6dWgxPwWyB6fvOb35SbLaJlvsvfDF6KxX8GhiyI+++/f2Ph8Uc/+tFlkXIBNf3OhlQxyUfSc7yl89SnPrV5+ctf3hxyyCFl99q1jHBlpRxUsC5+0qnBJAhEUE6CcvJYFARcwD0DVwSmiO7VV1+9Of7448ug2e/FfVFUNIUMgXkSIEiIyHe84x3F/++www5rvvvd78aKPwRX/tqWEtpyyy2blVZaqXnJS15SRDrG/WxmTtwE85lcYYUVmjvd6U7NPe5xj+ae97znzfvyyy9fPvNMb4GFsSr3QzbHDEoggnJQYjl+JglU6+SXv/zlZuONN25cgE0P3XDDDRkkZ7LFU6lhCPidECO/+tWvil+xhbKJINO1RKXo5VgqByNbb2Tf9KY3FY4PfehDm/PPP7/4Q+I912ZWhUh0vXIT7Ga40+67XXbZpUyDS7eftOfKO9+HQCuBCMpWGnm9ZAm4uIqAPO2004rfpMjuE044oXyWC++S7RapeBsB4sc096mnntqss846xTpZp1PXWmut5vDDDy+iMv7GbeDmeIvX9ddf3zzjGc8oN7PPe97zmu985zt9TU27Ppki/8lPflKsxPy9O+3S45+Zp3zN0Rj5emgCEZRDo8uJs0LABdn0kgv6M5/5zGa55ZYr00/f+MY3Yp2clUZOPUZCQMDaGWec0WywwQbNc5/73OLv96Mf/agsmG2qdv3112/e+MY3Nj/+8Y/Lb2okmS6BRFyDCHU+jwIBV1llleYTn/hE32veOp/Ydx2ba3dsthAYB4EIynFQTZqLioALrCU8PvKRjzQPfOADm/ve977Nq171qrJM0DRcfOtgYcDweqHLNM78B62f43Fp3cdZvkXVsbsUtp3ZILxYufjq7brrriVYzZqGBAwL2dVXX9284hWvaLbffvtGtDFL2CBpdyluz4+lP848Bkm7lqW1L3rd72bqWgCg5X7c1O61115l+bJB0ug3rxwXAuMgEEE5DqpJc1ERMCBad9IgyZn98Y9/fLG4LPTUkIHEQG3JDxYfuymrQSJAR9UQBkvlIbx/+ctflmj4UaVd05G+dfQERvUjRrQbNwVTfZWP1569nkG4Uv3//2tDU6ssYa28PKGlX79HFkoBJAJw9AVtUPuGlRD4UFpLUZ/13bg2eSpzXax7HHlhxVdUn5wrfeXBQ6BSZVt/r/2ylYZzPJnrrne9a/O///u/RaT3e/64WCfdEOiXQARlv6Ry3EwScBFnGWBd4Td5l7vcpTi3u7DPJUqcO6q9Ha50iSqDM1/OI488sjnmmGOat7zlLeUJPoOKXempj7oa+Az+Bkyf97M5l9AzDcd/jgDv99x+0neMsn31q19tPNv4C1/4Qs+gBHkT1ldccUWJxMfmqKOOao4++ujm4x//eKljv/lO03HqNYq9U52kW1cxeP3rX19YWfeQZZ7InKu/S7P2oSok9SF9ya79iB97/b5TOeb7mXrI77rrrivT75bL0adHuVVWfKrPOeecchPVq044fOtb32re9773lUcq6o/We/TbJdil189GIDvXQucrrrhiWZh8vk/P6SffHBMCoyAQQTkKiklj0RJwoXcRtwiwC7gLuaWCfNZtEDCoGsAcwxo2393ixgbj1vxquQxID3vYw5onPvGJZSqRn9qgA6i0pE8EOvfss89uzjvvvCLe+hms1Jel5gMf+EDxMbVmnqdutJZ3FB2ASCAQPTHkxS9+cZk27faYOHn7zgL0u+++e7kJ2GijjcpNwcEHH1wE0qjLN4o6dkpDOYkw9ZlvX3K+fklstwsg7Uj0POYxjynRwNYs9EQVNyl1Me1O5Wv/THmlrd96pJ82+NCHPlTazo1YP9bl9jT7fS9v6bsBfNnLXlbWXST4WBFHuclHnxe5bu3Gt7/97cXqSjh22rSfm6HXve51zYte9KLyqFaR1gKVBMhg38+mbp50s/baazfLLrts+b3xUcU7WwhMO4EIymlvoZRvrARc6IkjwTh3vvOdy6MWLcFBgHXaDDTE5OWXX17W4CM+BSHYLftBAPrvPevPiSee2Lz5zW8u/x177LHHFgsEK4TdMW9729uaa6655r/yVC7WJFa3NdZYo3nlK1/ZXHrppc1VV13Vc2BrL3MdgA38hMMOO+xQhKn16Hbbbbdineo1GCsHgfLBD36wecpTnlIEiClP4m/Um0HTIH766aeX9rDEicFVXurRvtXjWTM9BYSQdEPgyUbS6XROexrT8J5IMYUs2EVf0Sdq/9Gfar/xmdeOsfuuHlv7k/6lz1188cXFqtYqZPBi1dOfiG+WSXwFoxGg/fKSJr5uSrAmTP1+ttlmm1ImorWb8Jovb8JNeQ844IASYf7Wt761uDyMOj8sXAP8btywcIOx5mY3a6PjMcSXH6SbNsv0rLzyyqW8/QpCbLkNbLbZZs3tb3/78pxv4rn9hnO+HHN+CIyDQATlOKgmzUVBwCDgQi+a23S3C7joVYEHrQNxa2Wcw5JEYLGisRwSMSIzN9xww7Lsh2d/e/+IRzyiRItbAsSgS8Q9/OEPL4E/Fh2+173uVfJlgXznO99ZHvlY85I/QWkKl0XJM3m9Z8FQZuXoZ3Msq9ERRxzRPPaxjy2WPK/5Z1kE2TIlvu+0yaOK52233bYIygsuuKCUsxufTun0+5n8CAMWGaIcW8FRN954Y1eBohyYmLL1vHXRsYtNUBJJbigOPfTQ0h76iMAw1q2nPe1pZV3UBz3oQaWfbbLJJiW6Wp+yELYnq7CE3ec+9ymLWHtKijUhX/rSlxbXiFahpS8QPGuuuWZJg3Ah1uXfb3vWPuHm5ulPf3qx3gnEsQYiC7/yEFPE1ag3ZWRlJ6wFrugbfqvK3+/vYZAySRMfN3FY+/1edNFFXcW345XFOfqs363+SABj388mDWJdH+bP7WldZgZYg8dRx37KlGNCoF8CEZT9kspxEyHgomkQHEQ0DVsweREjBkcDePWf7OUf6ByDhsH4U5/6VJkKY+0xqLMMGUxNxVmcWDQs4cladOGFFxbfQIMhIep7jveiOQ0azmUJrJvBswpKAtXj0gYdpJXVOaYkn/SkJzVECQsX3y7CY5lllini4nvf+17N9r/+awOPhbOUCTHKWkrs9Ss+/iuxPt/UNrHA/M4771wsaQZULHzXafM5kc9/bdVVV110grK29Ve+8pXmzDPPLJZjNxl77LFHsVqyYLs5IaLceBA1+tSHP/zhhoXOOpDEjpuUu93tbuVGgdsAy5a+WrcqKC1GLhLbNHc3pvWc9v/KyqJP8PjNeGa0J0sRl6ZofaaM2mOUm3ISan6r6io/7hv696B1GKRc6kvMuVkhxLmcEIutXNvTc47fjd/tMIJSnVxHrDjhGrHnnnuOxWe5vdx5HwLzJRBBOV+COX9kBAwMLqaeTmPQarWujCyTloRc+Ik404esdaw8LIL80HptyqlsrHcE1mte85oyJWZ6THSrKdtNN920DEKsDY6zG2RZ0r72ta+V6TNC1CDMsmP6ctSCUv3kr3zV4nXWWWeVxadNxxmw+Hspc/umjgZSYo7ldeutty5P4zCoj3tTbm0g+IeVkvXLNHsvN4TFLCjxVGf10z/4IxJq/Hrd3LBe6k9EnBsZvxH9yX99hnhxw0KAumkgKj2LnkBtFT6jEJTKqC34+Om3LKH6FEu3/kTssWIr3yg3fNz4uBkjrk3x69vqNM7N70DepvHdILLA+q1qJ9912hw/rKCUnjrxx/QbddPn98clQrrZQmCaCURQTnPrLKGy1Qu3CycrAGHXKrDGgcIF2oBtoPD8W1OFIoT7FU3KzHLGukj4mH7zni8cCwphYNB3nN1A4T3xY+kd1iiDBgslX7hWIats0jJwDmuhJCa++c1vlsetEcyWIyEGRI4Ta6wu3nca/J1rqo51hDVVexCekxrU5M8VgZWSzx9LHP+1Tvljyw90sVooW/u2vieSngVRtLs+4AaE/6obAyKq9if/CTz9yW+FL6kbBDcPBCUr76gFpbwISG4efi+sp24APSqQWwZhy4Vi1DeD+qjgm+r/+cUvfnGifoV1QXf58xet1l9t0L7po/MRlNIknrk83O52tysC2m/B9aNTfu35530ILBSBCMqFIp98/4uAC6VB6Nprry3WDwEZRNc4NxdoF26C7Ta3uU2Z0uJj1u9gqMwGfBYLglI0J8sFq16roFQHxxqUBI8Y6L22ZqKoUAMzQdkqoEchKA3C8ttiiy2KNUnEKouqz1kf7ab8Ow1ShINpdlPdLKmEArHT6dhxtFGtP+uxqUY+bEREq0Cq+SrTLAtK1qpOglI/Nf36sY99rPQlNyQCZZ761KcWIT4OQemm4tWvfnXpT/yN67Qz/vqvGyblGmU/kRaRKujHFDKXEuUYZR61L3X7r06E/VZbbVVuArmu4N3pBme+glIZXBv8bu94xzuW64NAwW6/1W5lzuchMGkCEZSTJp78OhIwOLhou/MnIFimxi0oiRPWFf5qt7rVrYqQJTA7DRKdCq3MBORcgtJx0jRdeeCBB5YAGVP6BuHPfe5zZZrwpJNOKgKv5uP4+Vgo5UkwikxlVREAxJpk0Je27+2dNt+z3PKdNIAfdNBBNz9XuNPxw3zWLe+alu9Z34hIgSmCiM4999wiWOox9b9jl5KgVO9aZ0zcfLFquglgJbcigKWd3Jy1CnA3UG6YhvWhlKepX0Ln7ne/e/nvfbWc+d4+yk160ieOBc496lGPaj75yU927AejzLc9Lb8Jv3U3OKyzLMGskK5Z7Ztj52OhlB5rvN+dwBz5mVHQvqPm2172vA+B+RCIoJwPvZw7MgIulK2CchIWSnf8pglZCG9729sWYTdI0Iky9ysoqzgS7W2wZ8002Dvf8i3tjv4GpWEFpXLZaZxbRAAAIABJREFUDUqmSWtEueCJOgXfreGcV8vqWc0EpUGUNcZ3/WzKrm7S0aat53nts07ftactHZYpASb88yzG3SmQRJpLTVBi44bLzQzrpQAdDOxEuL3dgjYfQYmxPN3wEXZcRFiN3Rj5vLWN29txPu+ly5rvhmullVYq1nJuMZ2EXLd81Ft/0ydby1rr5HN7Fcad0nGs64Wpfb8Jy/oIyuvkLiKP+QpKN4PqzE91+eWXL4FX3B2knS0EppVABOW0tswSK5cLtkFCIAEL5SQEpYHK9DSxwvndNLWpJmXpZ3NcL0FZl08xUDmOH6ClRPbee+8iKNXXd0SAAa01XwPHoILS+QQjEcZSZbkTj5MUvS6a3DQdv0iDnbQ7DcrSUB7R6QIvRIf3E2ThPGXGlFiWt7U6+WvKT5oGZGUj4lmZrNennM7pNFBKEzeimB8n4SRApf3YWuZZ9aGsU95cFohI9cWS1c5yTtwt6uLe+hPXBN973bp5P6iFUh/xOEGWe+3Kf5LIscSWJYxY2H3Hok1ctbdNa/7DvFZXNxH8FgXNsdr1I6yc5zflXJZazxY3bWxGwvkEJms9HhbTN3VvSltdfef89g0L9cXbrIY1P/2O2jcM5isotaFIftZJ4t10v2tTe5u25533IbCQBCIoF5J+8r6ZgAu4C/YkBaUB5eSTTy5rRlqew3R0t4WLby5oywtlnktQEkuOsdwJ/yuDsUAXg3OvwcGgNKigNIASctYo5Bcq4EfE761vfesyKLEsiVIViWv6W7naN/mybLFKihhmHTTQSrvXpi4Gan6XhJ9B1xNG1llnnRKxzLdP2QSLiFq1ULQlfiyFI4iJ4Ow0iJvmE9ykPpZmIgzauTnP+bMuKAlrgodYZNG2jJObIWwIyrmsz7gNKij9HixLVNvS+qr8jVn0uVEIHNGWbpLcKBBjo9yU2W+F24b+KJBtrnrqDxixpgpuclOkPyqnGzquHL7jDrLxxhs366+/frmJtf4nwVoXZm/vj34buKurZbcED3by5RyFoPR7w9PC6AJzlJM1uL3vj5J10gqB+RKIoJwvwZw/EgIu3pMUlPIjnDxSTVSsKGjTh6aa2geSbhV0XCdBKTrXACDQhpD0hBrvRXPLx4Bk2q7X4DCMoJSeQYfVVVQ26xVBybHfoGohaCJEuYg7VpD2TRqsOoKFWEcEQLCMKE+3zXcsZ9YflI/lbVhyWCf33XffIhyJSEvfmCpk5SISiRNl62UZJlBYJYlU5bGmZntwkHaYZUHJwobbXnvtVfqTACnL5xAb+hNRTrDPJbS07aCCUppWPvDbOPzww0sbuEEhqDxBxiL5+hprfC8LmjaSP6GkTe1e+83rP3bft/czx+irppn50Xrts16/UWJS/3MDt/nmm5dpaqsd6Dv8R1m7BS75TRKQXE7c6PityMd71tb2PLx302Qqmri1OkSn5XzUYb4WSlyIXuUh4IlhPtgYZQuBaSUQQTmtLbPEyuViPWlByQLIEkdQ8lOy6PcoBCUrnQGXNZIFyX+PdWQhZGUjDMZhoawMWUUN7gQeKyUfSv6HpibVmWXWoNs+eOtyBmtLlFjTUB1YxZzTPrjW7ulzosNgzQrkkXjqJn3lYDljAar1Z0VUDtP/uLC+iCLv5rtqADWQWgeTACXW262ZyjDrgpKgIB4JGcKa+OHGwD0E33FZKLElrtx8mS6ukccivN0wsGD6Dn+/3079RBuyNBOz3By4Qphi9lpfc1Omjblj6Gut/VK61nXV9iyIhKHvO+WjT8qLFXHHHXcsZZWXvJVNHm7m/NbtBCG3ASsh1Eevuumrriqd8pAWizqh54bN2rPt9R6FoFQPvyNWfIJyvfXWK+JVXtlCYFoJRFBOa8vMYLlcoLvtLsLEjClvCxeL8ja91+341s+HQeV8VjURsQSXAcbj/kYhKIkmVhDTyyxrBnwDokXMRceaKhuHoMRBvQxGBnlWJYEMBkmWWHWrg7HjOm0sR9rAYKvMLJXS6nZ8HTxZMg3irGlVrBp8TVEaDPlxshixNhINBBAhaVkiQUosP53ykD5xLJhJXY488shbuCU4b1SCUlqj3Dsx7vUZ62v7OpSYmnJmodOfWCuJKyLdjmHrmqfd0tcvBrVQSgsPQkafJWD5T7LwsYb7vFufcp6+QODpB/qUtVBFSNvdJHCp0DdZtd2MsMq5DtRNn3XzoQ+7LkhLfp02+elzfn8ChuSp76q3c9zIuFESOe3mzs2ka4D+rg+y9FqgndhVbum1b8Q11qzD2oFgbT9WXvO1UEqDf7FyRlC2t0LeTyuBCMppbZkZKpcLM6HiYs9q1Wk3cLBUuEDzl9phhx3KHbnPOx1fPyMkug0wvRAqkwARA5xBmfXHACO/TgNJp7Qc12nKm0XFYMNHi1VHMADhShCMW1AqJx4EmkcsEsoGYtPMLIlzbdqJ1cYUIOuqKU0WzW4bQWFANoCz8FZrJjYGc1PsrGpEu+9ZtAgG5auPENQO0unEXV1MwROUfPY8N1oASGubO2++glIaytWrj9Y+1+9/ZepWr248uwlK/Yng4s/KSkc0CYrRn/gFjlNQKqt6yJtljr+xYC83fK3t0F4nfYkFT99nYXWTaGreb0IdWAKtJOB34ebF9DMrorzqpu9xGSEo+eMSWZ36ieN9TiDi5Kat+v763O49y6ryc78QCU8ManNC1nqexLv27VavKij1aW4c3A/ap8edOw5BSRC3sqmM8j8EpoVABOW0tMQMl8MF1wXbIGgattNOfAkUMaiwRAk04OvX6dj6mSdzsEJ0s271QmqAaRWU8jSVNZcfWmua0ugmKK2dSMQZnKTJssKH0eDpcXXjslAqn0FH+qwuhBwfRZGsBs+5tiooWZOUlQhUh24b6w8/MoM+oUCU1QHc4M7yxCJEMBAQ+oIB13m42L12TqfNsa2C0qLa7U/sce58BKXzsSF+TjvttJ59rva9fv67sdAOg4iAToJSlLep5kMPPbTUHWPC3BI2LJfjnPKubaLdRPtzg9CW+gWx1014YaqdXvva1xYhqB/oH9pJ+TFxw8E/k1XQNL6bD7+n1jSroJQnizbB2a2v+NwNIbcIS/rU8vlcmp4KRQiylhOyRJ/P7fq9vui/993ywKFaKPkLi8T2Wevxzo+grD0n/5cSgQjKpdTaC1BXF1oXdhd5A4dBodvOB89SNQI1CCEDZbdjfc6p3jRr9asapHrKxW+vWigtScJ60j449EpTGp0EZfuTcgww0rVUDkseocfSQkjZpGOQdYxjbf6z9g3z6EXiiFXUFDOOrDU1crUk3uNPFZSmIlmKPVayl6CsZTeQVzFZy29Q3W233Yqvn2lv9XfMIBsOVVDyuSRQCNXKSVrKMF9ByUplLUfP0BYF36vf9fOdNPQtQVlEYr9bN0Fpqps/a7150ndMOXNJYL2si75jYfO9tAi31s+GmfKWnn4uL/3JjQZrnvRr2u3104/0QaILC1Zv/bL1eG3Iysm/13Szxbv1tdZjqqAkZFkV9anW79vzVW9tqWztvy/i1e+8Lj+kH/VKqz1t7/1Gq6B0/ant25qOekVQdqKXz2adQATlrLfwFNTPQGK6ydqGAlY67fXZ0gSDC74BxhR0p2PrZyyeIjINdq0X9H6q7PhWC6WBkoW0fUDrlZY0+hGUjjO4mbJineQHKG8Dj+8MUqbf+BXWKXffDSsoWVoMeqYITeWzbLWLsG71ahWU/VgopaMOda/vpWPZk7pUElHLQlUH+W75t3+OQxWUprxNn47aQilPQteNCYu3flX72LD/paENBp2m7FdQ4kI4sfpyTyDWq8AjIt2wEJn+Vwsp9sMKSn6sfB5NF7vpYzWVnnZv33zmd+T3xNeTNdANTesNh3PqcZbuMoUuSKdddFZB2Y+Fspaj9sVaNv/9rkRn60OetlNnD+o5/f6vgrJOeRPWPqt5SUfbRFD2SzTHzRKBCMpZas0prYsLLIFhADTQdNpd8E2BfeYznymWMYOkwbjTsfUz6dUp00GrbgAg6qzZyIdylEE57RZKZZOfchuATL0pOy525RB9yk/QwO1Ynw8jKGs+XAhYdUzlW9C8CtW5OFVBib9Bkw+lcwfZlEH9WKUt08L/TYBQq0XIMfLCRF27bb6rQTlE/7gEpfIQPCydtX/N93+d3pV2v1s/glJa0iQUiWDCsQbI+Fx7cQdhDWeVk6ZtWEEpTW4N1py0yLZpdu+71cvnhKCFv61qQID6TejnPifAlEXbes3Kx92Eb6zPW7cqKN1kzuVD2Xpe62v5yHu77bYrgpjvKV9J/c+mvPLVXu2CtjUdrxdSUGJebw7ay5X3ITANBCIop6EVlkAZXLR77S76LvDtUd4+t/c6dxh80jPdZtFmVrxRCkq+c/wWWagI3rqph/cGrVovg5ioXoEzptBMwyub74cVlKym/E9NEVs2yCMX5dPP1iooWYXmivJWVoMx0VIHY2Xn42cqUxkIU0yITJtzHGsaVISvenbbpE0MmD4mKlit3XhIo25eqx+roGVW+OvVqeF6TD//pTOOvZ+86zGdBCXx0zrlXY9VViJYnyI0vNd+rJCiqD3NxXJO1YKG5TAWSuexSArIsVyRxcLrjU8tS+t/5VAmLgREqMAhwTCs1Fw4BLKwWGoj7eZ3yAqt7M5t3Yjj+tjFGuXdfkzr8fqevlVFq2OVn3+sCHUrLehLVYA7FzsWRTMo+qRydNvUi1UYC79xN4ft5VaGcVgoIyi7tUo+nxYCEZTT0hJLvBwu/C7sBOUkHr0oP0KGjyChQlQOs7C5NJzH/1MABjHXTVBqYvnWQY71xYDEMknQ8t8zsPp+WEFp8DSYseQRc/zrCFaDbD+bNjDYE2UsnIJgWi2L7WkYTNVbwIblVtTJZ6a3LXwtIIcfXZ2Sdb66mbaWBx9Yde621fJYJ1B5TEkb1DGqm9ejEJQ1vYX830lQ1oXdW30oW8tY+4tzBX9xqXAjYbpZ0IjPHTOMoHSeviMd67X6rUhfP++1aTdl4UNrtQCBMMSo5Xb8VlgL9VEWVGk5Xl7tm7bm1qLt+fRynVGPTsf6XF/VD4nWKhqlwYorDb8JfdoNj34oHZZoFlJW+SrA28tR3xO43HMISnXQz9vLPgpBqS7SrutQipIf1H2iljn/Q2BSBCIoJ0U6+fQk4MLuwjxJQcnSxc/LQEn4GLDbrV+9Cq3MBkMRrwZJ/mrOJ3pYL1gyqvhxrN1AQfwQfaYB+aXx67Ib6KTnuGEEpfNYrDBkRZKmJ6qwSmHbz6Z8LEZEg8hbT7oRWevz9k1+BjnWMAFUxKFln9RB3QXiEBEGXmWqlh8Cx6BPaNZo4fa063vHCu4wTcnSaamhdouQcsyCoFQPljUiCzMuA/qTxb+t+9j6LG/H1h0PQkokv1US3JDpz6a8+eZW7tpwUAulPIgo7eSmh8DhtoF3r63WRf7OtcQOQccFg+sCq6XfHR9b0er1d9Kepv5shQhT59Zk1G/a27+eo5xWG/B0HIFTrN+soCzcO+20U+mLhKAIfcf6jWHihshyVPybW3nVdOt/dXLz47ehL1oGiWiVTus2CkGp3qylyrvMMsuUOsmr0++wNe+8DoGFJBBBuZD0k/fNBFysJykoZWwK1rQcq4nIcsED/LiUpddmwDAAGsRNBe6yyy7Fv8vj3ohKU8zElClfgxVrnHQNbDXww7I8pgP5F6622mploLZkjTLJXx6DTnk7jwBjneG3RgAQGAbB9kGvW/2kwcpoapllifW02zOaDW6elkJkmEq0HiIhSbQIBHI+YWNNUYJS2eymH/muGvRZ3wye3TY8CG+Ld6sTH9v2QVWZF7OgVH4MiEfTmqZ4sWE9Zy32uEHi21QtcaU/2bUrQU9IEpvOIUD0Z+zdDLAS1psJ3IYRlPqu38Yd7nCHMo2uz1eR2qnd1Meuz2lv57spYP3jlkC8+X3on/q/5bTcxHTqoz7zm2JtJUj5BrMoSr91814+bqA8QYhoPfjgg8u5rON8eYlYopR/abWkO6eKUAuoK0d7/6r5KEu1vEvHb6uT9d5x853yxhczwtW1yY2n60e3stUy5n8ILCSBCMqFpJ+8byZgQJi0oDTYmXIm6DwC0PSwi3b7YHVzIf9vyrpakTxC0VImBsXqJ2bQ94Qc703ReW8qjTCTvqkr0dOiZeVpkGZ94R8mypWwkL9BaRhBSVixbNVpfFHz1erZWo9ur+VdpwhF2hOJ3RZFV0b+fepp0GOl5DfqiUPW4TSF73zBFO9+97uLpc3gzpLJp840ufJKp9OmLKYmCQNPHmIJNaC3H++4xSwo9XvCj9+r/kI8EUV8IDHUx7hksDjzpcSBBZMFkuVP1LI+6Ak2rFmmdYlKQTEET+U1jKB0Lgu1dWE9sUVbEr3dhI3j9R9it7atYwkkvzdikNhiabY0lTqJ8O42nSs94tkNmHq5efO+1qn2G31AXVkZq6DUD4lYVlAW3m222aYINGmxRLrJwcjvUh9zs9fN+ikf7UTk6ed4mPpm6WzflG2+ghI7Kwz4HQuE8phHTLtxby9D3ofAQhCIoFwI6snzFgQMCJMWlMQbi42B+1a3ulURPwbP9sGqtbDKyWrGCimIhjgkCPmsET3SYr0woLPcEQemIH1eLZGsSCwP9TifW7jaoFrz9n9QQekcVi5WHFOL8lZO5e13Uz9cTLMavNWNj6iy+K51897g7ok88lJHU9MGb0KWQCaS6rqO3AB8R1zzySMulLk93ZqH77QHQY6nKUwCs/147xezoMSbpZfPKau1fqEv2Yl1/aNaHvUz/ckNiNf6mDbCx3+7z9zUuBHApfIaRlD6TeoL8iRsiMBOgq62mZstbetmi/hSt7oph12a+pPlglgdWRW7BZw4nkDlRmKKnGsJ8d2abk3fcQSkmxnsRKMTi0S5qfLLLrusCDPWbsJcf+Sbe/TRRxcxyVe0sqpp1v8+J4jNaGgLaRKknXyT9dv5CkoWVGKX4CakzXZgJu1sITCtBCIop7Vllli56kDjIm2w5PPE4jDOzcBmOs10GEHpv8Gq10VbOQ3MhI3pbBaz66+//ub/Xrfuvq976+et5/iemGydRlSGQQWlcom+tdQPy4ZpMkKlNd1+eKoj3zMR3gQKcaCMnawjGDrWNDYBYfrVNLapQwMwTs5lCTao8wtTV9/NxZk44a/HCkc81TX/2uugvItZUOKg/CL8Wclq38Ctvc+0ftfte8fox24uWhkPIyi1k6fOuHEibrgftIrU1rbQDvI8/PDDiwWPb2QnweU4NzmnnHJKEcKWzOpmfXOsOrjxUwbClmhUhvbNcdLVv7hp8OetbijqQXC6QeE2oJ+64cFKHyVQ5dVtk7bflkAiqx+wqnYrs2PnKyhNxbNWW9LMDQK/Wr+HXmXsVvZ8HgKTIhBBOSnSyacnARdK4oQYYaUxrTduQWmANYizYph+Zg0yAM0lwOog53xlnu8uHYNQ62DhfS9B6VjnsWQQxabqDbIGSwE5pkDrQC2tQTZpS8tC6/zyTO953WlAqywMyMSDgdtredYdn/odtsrtPHu3zbksYQceeGDxB9xjjz2K2JJW+1bLO99lg9rTneT7ynGcfUravXwolUEbE06EFwsydwkWPCKKsOHioA06tZ02I4S0ld8wa55+5PN6vP/6AiHHx9NNHCt2u/htZe8cfYEFlyVW5DgxV9NsP1Y99TN9UV7KW/ti+3e1r3ZKqzVd6blhImj58ooKJ16l2775bD6CUlkE4HA54cLA8u+62I17e/55HwILRSCCcqHIJ9//IuAi6mLPsiJ60mLcBplxbi78rGuCBEQjC2QQAWogHWRT9vnsnfJStl6C0vfEJD8ugnjPPfcsgzSrkEGaNYcPGR8vZRt0M3gR2yLPCW1WJ0JDG3Xa2uvffkzr9+3fdXpPCLBq8nsjTkx5Eied6uIz3y1mQdnKoJXVMK9b02p9re16CUoCTNATQcg6xs9RUBAfRD6cHp1K6HRqA/noM5b14e9nKluwDSHk90S42bWTKXS/b1PPrYFD3dKVtv7Aqsgnl8VaOYm8bue0cmtlUF/P9X09rv4nrAWqcUfgg8kKimen/OcrKJ2POzcSN7r8VuvMSaf8ahnzPwQWmkAE5UK3QPK/mYALqYGDlcMFvJt4ufmEeb6Qn2kwlhQ+hyKSWT9M1y70pmy9BKXB25SnAVaENZ85YpJfmmlqg7rpZYP4MJuBCxtrWPI1Y6kUPd5N1A2TR7dz1J1Fit+YgJMDDjigq3VSGso6S4KyG5f5ft5LUGKu3x9yyCHFXYK/Ih8+T7FhodanWKlZ5bqJGv1FlL+IdDcBAq8EyfiM/6Ldb81n/GqtQ0pwErLd0qx1Vj7XBUt7ucFhuWaV9zsY56Zc8hDEQ8jyy7SmZS8OyjofCyWhbLqeeOW36qaq0/JE46x30g6BYQhEUA5DLeeMlcBcg8uoMq+DhUFNkIwoWU7/pvsmVYZudTEo9RKUhDfBaNBhxTDQCcLge0pcWvtvkOjuTuWoIuOoo44qy7xYJoWIHafQx920K789VjJrChLKvQZw50RQdmrB//6sl6D0HdHCYmiFAtZ6wVgs0yxlosqJpF4CThsJAhPMJUJZEJUbNC4Ylh0SjGW5KDMQlkYSiKMf62dzbdrYzRHfSIFdgr+I02Et8HPlV79XNj6W1p7EwZO1WOp7cXDOsIJSPdXJ7EJds1M7zPe3XOuT/yEwTgIRlOOkm7SnnoCLv+krVhXWAP5RplqHteyNqsLKRVDyXzPlZXBmQSKcDGbKZxrMNCR/SVHirDaEpTUg1clxBqhhN+eylhCRxKQljwx0cw2o88mPwBDgIz+igQuCencSsbV8hAyLpuh2EbjDPnpx2HIvlvOqoBT0xuql/2DHQqiv8Fnmz0g4+R0Ql1YyIAQFsMwl/qRBSElX3+Wywm3CNDjfZFPWboLcsPleH9aG/fZRx3HzYCl1o0GkCvTym/B7GfVW8yNc+TO6WVMXv4n2/Byr/m6GTPFzLXBjx+eTv2in/tupvNJhiSXCBeS40TW938l/udP5+SwEFpJABOVC0k/eC07ABZwFgk8XPzEXcAsdj9vyMVfFq6BkGWGFtHSOcln83IBtcDdweSydKURLrwjCIUAJQIPeKDblMJgZ1FieLLfCCsViMurNgCxyXh0IHpZRdfV5p40QIlD4kfJvE8hlqjaCshOtpoiaGi1NMOJsfVBrgxJq2llQlz7HsujZ29wNWIv1Nb+VXpvvCSe713XXfvqjnYjUp+ZKq1s+0haQYxFyonKfffYpfcDvYdSbKXzWcZZyAg8n4rVT2dWRUD7vvPPKTZCVFupST4MISvXz+7XWpTVq/Rd0J/1O+Y66zkkvBOZDIIJyPvRy7qIn4CJNmLCgsCZYcLw+X7rdCjHJyioXCw+LkehagxPfMQNMXTbF4MPCZIA1fWhAI/QM3KMafKSDA4FtQGU5JCwJvVHlgau0DOACQSzJot6CN7r51zmeyCGw+fhhw88PJ8EgmSK8ZW/VjpYl4pdKeAvcqj6qpruJFsLMDRZLoz7FWqZd9LVhN23Vug+bjvOkQ5QSWUSlGymiWP8c5SYflng3awLeLNtDdHfj4DdnOt61owbFYSwqXIBNt/Pay0zUs4jqzxZo5zOqPfo9vz29vA+BSRKIoJwk7eQ1lQSqdYH1zTSTABSWGgPXQm0GNIML53zWDguF21lSTbu1WmQcSyzYvR7HJm3iVd5EXLdlW4bNu9YXd1ae6l/XrT4+N4h7DCBLmzUz7VhZr5IIyvbfBLShaW1uC/oSXgKfPN2IAPe9DVuv7d34/3fKk3+n7QXlEHp+I6Nub3W3AoSnTrGCE6y9ePiO24XjMa390Y0R5r6fa8Oae4d1X6356ebIlH63m6q50sv3ITBpAhGUkyae/KaOgIs968Pb3va24o/IciNamgVsobY6qBOV/M0M+HblHKUFst/61fLIm7Akwke9aQdCmTio/nW98lAmx7PkVj5YOb+fAbxX2rP4HV7aTR9q5aU9WcB8v1g2ZVVmvw/7ONpb+tjoY9LvxaeWx/Gtv1d9s5++jLvjPIhAYKAVJ/iImnVYbG2zWPpQyjl6AhGUo2eaFBcZAYOBi7mlQUwpsw5wwGcBGcdANQweZew1oA2T5iydEz6DtWbllT41GLd+j658Bzme+DQDwfXGQvKeSlQto/2mk+NCYCEJRFAuJP3kPTUECEdTVgJBPLaQsPRMYtNN2UIgBEJgnARYjgXvCABafvnlywoH3Ev6tW6Os2xJOwT6JRBB2S+pHDfTBFgUTJ3xWVpvvfWKhcAyPHwFp8VKOdMNkMqFwBIl4NrDEmlpMEs2sU4KOOKWMNdU+xJFlmpPKYEIyiltmBRrsgRc1PkqEZCCPDw6jpVSwIKlQrKFQAiEwDgIsE5aKkiE+IorrliWQxIxvhC+0uOoX9JcOgQiKJdOW6emcxBotVJ6PrZlPzzXuNdaiHMkma9DIARCoCsB15y6Du5DH/rQsvSVdVWzVFBXZPliiglEUE5x46RokyfASulibm076xpaA/KYY44pS4i4+GcLgRAIgVEREEFu2SNPvDLVbaF2a4XGOjkqwklnkgQiKCdJO3lNPQGi0cXcE1te+cpXlojLLbfcsrn00kuLj2VE5dQ3YQoYAouCgKlu6616xCUx6bGp1lC19FD8thdFE6aQbQQiKNuA5G0IEI3WMhSgYy04Cwx7rCE/J1GX2UIgBEJgPgQIRv7aHnNpFuRhD3tYc8opp2Sqez5Qc+6CE4igXPAmSAGmkYCpbwsUn3XWWc2GG25YLvqHHnrozc/VncYyp0whEALTT4CYdG0544wzylO5PKrx2GOPbTz+MssETX/7pYTdCURQdmeTb5YwAVZKotKj0DxBZ5111mlWX331EgHuGb++yxYCIRACgxBwXfEELi4022+/fXmW+mGHHVZmP/hTxqVmEJo5dtoIRFDEqoSMAAAE/klEQVROW4ukPFNDwMWdxYCAPOmkk8r6lBtssEFz/vnnj/zZwVNT6RQkBEJgbAT4TV5zzTXNrrvu2jzucY8rftre53GhY0OehCdIIIJygrCT1eIjQFQaBPg7ifw+4IADmksuuaQ4zi++2qTEIRACC0nAteSqq65q9t9///KYRU/DISZjmVzIVkneoyIQQTkqkklnZgm42JviFv1tQMjTK2a2qVOxEBg7AdcPMx/2ej0Ze6bJIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQCCCcgKQk0UIhEAIhEAIhEAIzDKBCMpZbt3ULQRCIARCIARCIAQmQOD/Axpn+9rPr94EAAAAAElFTkSuQmCC" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recordar que en las esperanzas de la segunda ecuacion son sobre las variables que estan en la expresion de MSE.\n", + "\n", + "En este caso, por construccion **sabemos** el valor de `E[f(x)]`.\n", + "Es decir `E[f(x)] = x^2 + x` (ver funcion sample_data)\n", + "\n", + "Con eso podemos calcular el sesgo y la varianza para este caso" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [], + "source": [ + "def bias(model, x, y, iters=1000):\n", + " ideal_y = (x**2 + x).squeeze()\n", + " errors = (\n", + " ideal_y - expected_prediction(model, x, iters=iters)\n", + " ) ** 2\n", + " return errors.mean()\n", + "\n", + "def variance(model, x, y, iters=1000):\n", + " ideal_y = (x**2 + x).squeeze()\n", + " # repetimos la esperanza en `iters` filas para facilitar el computo\n", + " ideal_y = np.repeat(ideal_y.reshape((1, -1)), iters, axis=0)\n", + " \n", + " # sampleamos iters predicciones de modelos\n", + " ys_hat = sample_predictions(model, x, iters=iters)\n", + " \n", + " # Estimamos la varianza por cada par (x_i, y_i) del dataset, luego promediamos las varianzas\n", + " return np.var(ideal_y - ys_hat, axis=0).mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "De esta forma podemos calcular el sesgo y la varianza de una regresion lineal" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.393858748727712" + ] + }, + "execution_count": 171, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bias(\n", + " LinearRegression(), \n", + " *sample_data(1000, x_matrix=True)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.10906147472344568" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variance(\n", + " LinearRegression(), \n", + " *sample_data(1000, x_matrix=True)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Que pasa si jugamos con regularizacion L2?\n", + "\n", + "A la regresion con regularizacion L2 se le suele llamar `Ridge`. El parámetro lambda en scikit-learn se llama alpha (a mas grande, mayor regularizacion)" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.408022545024507" + ] + }, + "execution_count": 181, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import Ridge\n", + "\n", + "bias(\n", + " Ridge(alpha=500), \n", + " *sample_data(1000, x_matrix=True)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.05763412973111156" + ] + }, + "execution_count": 182, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variance(\n", + " Ridge(alpha=500), \n", + " *sample_data(1000, x_matrix=True)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Y podemos calcular el sesgo y la varianza de un decision tree" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0012359837781728128" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bias(\n", + " DecisionTreeRegressor(min_samples_leaf=1), \n", + " *sample_data(1000, x_matrix=True), \n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.014141026848157" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variance(\n", + " DecisionTreeRegressor(min_samples_leaf=1), \n", + " *sample_data(1000, x_matrix=True), \n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Se puede ver que el decision tree tiene **muchisimo menos sesgo**, pero tiene una varianza 10 veces mas alta.\n", + "\n", + "### Qué pasa si utilizamos min_samples_leaf = 10?" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.12437374093975646" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bias(\n", + " DecisionTreeRegressor(min_samples_leaf=10), \n", + " *sample_data(1000, x_matrix=True), \n", + " iters=1000\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.4022126724472044" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variance(\n", + " DecisionTreeRegressor(min_samples_leaf=10), \n", + " *sample_data(1000, x_matrix=True), \n", + " iters=1000\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "65c671c52ea64930bc656c43a45dbb75", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=10, description='min_samples_leaf', max=50, min=1), Output()), _dom_clas…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from ipywidgets import interact, widgets\n", + "\n", + "def draw_predictions(min_samples_leaf):\n", + " ys = sample_predictions(\n", + " DecisionTreeRegressor(min_samples_leaf=min_samples_leaf), \n", + " oos_X,\n", + " iters=100\n", + " )\n", + " plt.plot(oos_X, ys.T, color='k', alpha=0.05);\n", + " plt.scatter(X, y)\n", + " plt.plot(\n", + " oos_X, \n", + " expected_prediction(\n", + " DecisionTreeRegressor(min_samples_leaf=min_samples_leaf), \n", + " oos_X\n", + " ), \n", + " lw=5\n", + " )\n", + " plt.xlim(-5, 5)\n", + " plt.ylim(-4, 12)\n", + " \n", + "\n", + "interact(\n", + " draw_predictions, \n", + " min_samples_leaf=widgets.IntSlider(min=1, max=50, step=1, value=10)\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Un modelo con el sesgo perfecto" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.pipeline import make_pipeline\n", + "\n", + "# Ajustamos un modelo cuadrático\n", + "model = make_pipeline(PolynomialFeatures(), LinearRegression())" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ys = sample_predictions(model, oos_X)\n", + "plt.plot(oos_X, ys.T, color='k', alpha=0.05);\n", + "plt.scatter(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En este caso, al elegir el sesgo correcto, el modelo generaliza inclusive a datos nunca vistos de forma correcta.\n", + "\n", + "\n", + "En este caso tenemos un modelo de bajo sesgo y baja varianza (algo muy raro en la práctica). \n", + "\n", + "Si bien este es un ejemplo de juguete, sirve para ejemplificar como un sesgo introducido de forma correcta al problema puede ser muy beneficioso en la calidad del modelo final" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.344946751807706e-05" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bias(\n", + " model,\n", + " *sample_data(1000, x_matrix=True), \n", + " iters=1000\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.029742082291003166" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variance(\n", + " model,\n", + " *sample_data(1000, x_matrix=True), \n", + " iters=1000\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}