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3. Applying AI to EHR Data /
4. Building, Evaluating and Interpreting Models /
Class Code Walkthrough /
Lesson_4_screencast.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Build Regression Model with TF DenseFeatures"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Overview\n",
"- Used preprocessed dataset from TF regression tutorial https://www.tensorflow.org/tutorials/keras/regression\n",
"- Used TF feature column API and TF DenseFeatures instead of the methods used in tutorial"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dataset Preprocessing From Tutorial"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"dataset_path = tf.keras.utils.get_file(\"auto-mpg.data\", \"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MPG</th>\n",
" <th>Cylinders</th>\n",
" <th>Displacement</th>\n",
" <th>Horsepower</th>\n",
" <th>Weight</th>\n",
" <th>Acceleration</th>\n",
" <th>ModelYear</th>\n",
" <th>Origin</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>393</th>\n",
" <td>27.0</td>\n",
" <td>4</td>\n",
" <td>140.0</td>\n",
" <td>86.0</td>\n",
" <td>2790.0</td>\n",
" <td>15.6</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>394</th>\n",
" <td>44.0</td>\n",
" <td>4</td>\n",
" <td>97.0</td>\n",
" <td>52.0</td>\n",
" <td>2130.0</td>\n",
" <td>24.6</td>\n",
" <td>82</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>395</th>\n",
" <td>32.0</td>\n",
" <td>4</td>\n",
" <td>135.0</td>\n",
" <td>84.0</td>\n",
" <td>2295.0</td>\n",
" <td>11.6</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>396</th>\n",
" <td>28.0</td>\n",
" <td>4</td>\n",
" <td>120.0</td>\n",
" <td>79.0</td>\n",
" <td>2625.0</td>\n",
" <td>18.6</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>397</th>\n",
" <td>31.0</td>\n",
" <td>4</td>\n",
" <td>119.0</td>\n",
" <td>82.0</td>\n",
" <td>2720.0</td>\n",
" <td>19.4</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MPG Cylinders Displacement Horsepower Weight Acceleration \\\n",
"393 27.0 4 140.0 86.0 2790.0 15.6 \n",
"394 44.0 4 97.0 52.0 2130.0 24.6 \n",
"395 32.0 4 135.0 84.0 2295.0 11.6 \n",
"396 28.0 4 120.0 79.0 2625.0 18.6 \n",
"397 31.0 4 119.0 82.0 2720.0 19.4 \n",
"\n",
" ModelYear Origin \n",
"393 82 1 \n",
"394 82 2 \n",
"395 82 1 \n",
"396 82 1 \n",
"397 82 1 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',\n",
" 'Acceleration', 'Model Year', 'Origin']\n",
"raw_dataset = pd.read_csv(dataset_path, names=column_names,\n",
" na_values = \"?\", comment='\\t',\n",
" sep=\" \", skipinitialspace=True)\n",
"raw_dataset.columns = ['MPG','Cylinders','Displacement','Horsepower','Weight',\n",
" 'Acceleration', 'ModelYear', 'Origin']\n",
"dataset = raw_dataset.copy()\n",
"dataset.tail()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"MPG 0\n",
"Cylinders 0\n",
"Displacement 0\n",
"Horsepower 6\n",
"Weight 0\n",
"Acceleration 0\n",
"ModelYear 0\n",
"Origin 0\n",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"dataset = dataset.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'})"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#split 80 20 train test split\n",
"train_dataset = dataset.sample(frac=0.8,random_state=0)\n",
"test_dataset = dataset.drop(train_dataset.index)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',\n",
" 'Acceleration', 'ModelYear', 'Origin'],\n",
" dtype='object')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_dataset.columns"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MPG</th>\n",
" <th>Cylinders</th>\n",
" <th>Displacement</th>\n",
" <th>Horsepower</th>\n",
" <th>Weight</th>\n",
" <th>Acceleration</th>\n",
" <th>ModelYear</th>\n",
" <th>Origin</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>28.0</td>\n",
" <td>4</td>\n",
" <td>90.0</td>\n",
" <td>75.0</td>\n",
" <td>2125.0</td>\n",
" <td>14.5</td>\n",
" <td>74</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>282</th>\n",
" <td>22.3</td>\n",
" <td>4</td>\n",
" <td>140.0</td>\n",
" <td>88.0</td>\n",
" <td>2890.0</td>\n",
" <td>17.3</td>\n",
" <td>79</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>12.0</td>\n",
" <td>8</td>\n",
" <td>350.0</td>\n",
" <td>160.0</td>\n",
" <td>4456.0</td>\n",
" <td>13.5</td>\n",
" <td>72</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>378</th>\n",
" <td>38.0</td>\n",
" <td>4</td>\n",
" <td>105.0</td>\n",
" <td>63.0</td>\n",
" <td>2125.0</td>\n",
" <td>14.7</td>\n",
" <td>82</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>331</th>\n",
" <td>33.8</td>\n",
" <td>4</td>\n",
" <td>97.0</td>\n",
" <td>67.0</td>\n",
" <td>2145.0</td>\n",
" <td>18.0</td>\n",
" <td>80</td>\n",
" <td>Japan</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MPG Cylinders Displacement Horsepower Weight Acceleration \\\n",
"146 28.0 4 90.0 75.0 2125.0 14.5 \n",
"282 22.3 4 140.0 88.0 2890.0 17.3 \n",
"69 12.0 8 350.0 160.0 4456.0 13.5 \n",
"378 38.0 4 105.0 63.0 2125.0 14.7 \n",
"331 33.8 4 97.0 67.0 2145.0 18.0 \n",
"\n",
" ModelYear Origin \n",
"146 74 USA \n",
"282 79 USA \n",
"69 72 USA \n",
"378 82 USA \n",
"331 80 Japan "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"train_labels = train_dataset['MPG']"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Cylinders</th>\n",
" <td>314.0</td>\n",
" <td>5.477707</td>\n",
" <td>1.699788</td>\n",
" <td>3.0</td>\n",
" <td>4.00</td>\n",
" <td>4.0</td>\n",
" <td>8.00</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Displacement</th>\n",
" <td>314.0</td>\n",
" <td>195.318471</td>\n",
" <td>104.331589</td>\n",
" <td>68.0</td>\n",
" <td>105.50</td>\n",
" <td>151.0</td>\n",
" <td>265.75</td>\n",
" <td>455.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Horsepower</th>\n",
" <td>314.0</td>\n",
" <td>104.869427</td>\n",
" <td>38.096214</td>\n",
" <td>46.0</td>\n",
" <td>76.25</td>\n",
" <td>94.5</td>\n",
" <td>128.00</td>\n",
" <td>225.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Weight</th>\n",
" <td>314.0</td>\n",
" <td>2990.251592</td>\n",
" <td>843.898596</td>\n",
" <td>1649.0</td>\n",
" <td>2256.50</td>\n",
" <td>2822.5</td>\n",
" <td>3608.00</td>\n",
" <td>5140.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Acceleration</th>\n",
" <td>314.0</td>\n",
" <td>15.559236</td>\n",
" <td>2.789230</td>\n",
" <td>8.0</td>\n",
" <td>13.80</td>\n",
" <td>15.5</td>\n",
" <td>17.20</td>\n",
" <td>24.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ModelYear</th>\n",
" <td>314.0</td>\n",
" <td>75.898089</td>\n",
" <td>3.675642</td>\n",
" <td>70.0</td>\n",
" <td>73.00</td>\n",
" <td>76.0</td>\n",
" <td>79.00</td>\n",
" <td>82.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% 50% \\\n",
"Cylinders 314.0 5.477707 1.699788 3.0 4.00 4.0 \n",
"Displacement 314.0 195.318471 104.331589 68.0 105.50 151.0 \n",
"Horsepower 314.0 104.869427 38.096214 46.0 76.25 94.5 \n",
"Weight 314.0 2990.251592 843.898596 1649.0 2256.50 2822.5 \n",
"Acceleration 314.0 15.559236 2.789230 8.0 13.80 15.5 \n",
"ModelYear 314.0 75.898089 3.675642 70.0 73.00 76.0 \n",
"\n",
" 75% max \n",
"Cylinders 8.00 8.0 \n",
"Displacement 265.75 455.0 \n",
"Horsepower 128.00 225.0 \n",
"Weight 3608.00 5140.0 \n",
"Acceleration 17.20 24.8 \n",
"ModelYear 79.00 82.0 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_stats = train_dataset.describe()\n",
"train_stats.pop(\"MPG\")\n",
"train_stats = train_stats.transpose()\n",
"train_stats"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"categorical_feature_list = ['Origin']\n",
"# for the sake of this exercise treat Model Year and Cylinders as numerical but could process differently\n",
"numerical_feature_list = ['Cylinders', 'Displacement', 'Horsepower', 'Weight',\n",
" 'Acceleration', 'ModelYear']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"def norm(x):\n",
" return (x - train_stats['mean']) / train_stats['std']\n",
"normed_train_data = norm(train_dataset[numerical_feature_list])\n",
"normed_test_data = norm(test_dataset[numerical_feature_list])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"norm_train_data = pd.concat([ train_dataset[categorical_feature_list + ['MPG']], normed_train_data], \n",
" axis=1, sort=False)\n",
"norm_test_data = pd.concat([ test_dataset[categorical_feature_list + ['MPG']], normed_test_data], axis=1, sort=False)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"norm_train_data.to_csv(\"mpg_train_data.csv\", index=False)\n",
"norm_test_data.to_csv(\"mpg_test_data.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Origin</th>\n",
" <th>MPG</th>\n",
" <th>Cylinders</th>\n",
" <th>Displacement</th>\n",
" <th>Horsepower</th>\n",
" <th>Weight</th>\n",
" <th>Acceleration</th>\n",
" <th>ModelYear</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>USA</td>\n",
" <td>28.0</td>\n",
" <td>-0.869348</td>\n",
" <td>-1.009459</td>\n",
" <td>-0.784052</td>\n",
" <td>-1.025303</td>\n",
" <td>-0.379759</td>\n",
" <td>-0.516397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>282</th>\n",
" <td>USA</td>\n",
" <td>22.3</td>\n",
" <td>-0.869348</td>\n",
" <td>-0.530218</td>\n",
" <td>-0.442811</td>\n",
" <td>-0.118796</td>\n",
" <td>0.624102</td>\n",
" <td>0.843910</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>USA</td>\n",
" <td>12.0</td>\n",
" <td>1.483887</td>\n",
" <td>1.482595</td>\n",
" <td>1.447140</td>\n",
" <td>1.736877</td>\n",
" <td>-0.738281</td>\n",
" <td>-1.060519</td>\n",
" </tr>\n",
" <tr>\n",
" <th>378</th>\n",
" <td>USA</td>\n",
" <td>38.0</td>\n",
" <td>-0.869348</td>\n",
" <td>-0.865687</td>\n",
" <td>-1.099044</td>\n",
" <td>-1.025303</td>\n",
" <td>-0.308055</td>\n",
" <td>1.660094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>331</th>\n",
" <td>Japan</td>\n",
" <td>33.8</td>\n",
" <td>-0.869348</td>\n",
" <td>-0.942365</td>\n",
" <td>-0.994047</td>\n",
" <td>-1.001603</td>\n",
" <td>0.875068</td>\n",
" <td>1.115971</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Origin MPG Cylinders Displacement Horsepower Weight Acceleration \\\n",
"146 USA 28.0 -0.869348 -1.009459 -0.784052 -1.025303 -0.379759 \n",
"282 USA 22.3 -0.869348 -0.530218 -0.442811 -0.118796 0.624102 \n",
"69 USA 12.0 1.483887 1.482595 1.447140 1.736877 -0.738281 \n",
"378 USA 38.0 -0.869348 -0.865687 -1.099044 -1.025303 -0.308055 \n",
"331 Japan 33.8 -0.869348 -0.942365 -0.994047 -1.001603 0.875068 \n",
"\n",
" ModelYear \n",
"146 -0.516397 \n",
"282 0.843910 \n",
"69 -1.060519 \n",
"378 1.660094 \n",
"331 1.115971 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"norm_train_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load Preprocessed Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Use TF Dataset API and Feature Column APIs instead"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"train_data = pd.read_csv(\"mpg_train_data.csv\" )\n",
"test_data = pd.read_csv(\"mpg_test_data.csv\" )"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"#adapted from https://www.tensorflow.org/tutorials/structured_data/feature_columns\n",
"def df_to_dataset(df, predictor, batch_size=32):\n",
" df = df.copy()\n",
" labels = df.pop(predictor)\n",
" ds = tf.data.Dataset.from_tensor_slices((dict(df), labels))\n",
" ds = ds.shuffle(buffer_size=len(df))\n",
" ds = ds.batch(batch_size)\n",
" return ds"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"PREDICTOR_FIELD = 'MPG'\n",
"batch_size = 128\n",
"train_ds = df_to_dataset(train_data, PREDICTOR_FIELD, batch_size=batch_size)\n",
"test_ds = df_to_dataset(test_data, PREDICTOR_FIELD, batch_size=batch_size)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"sample_batch = next(iter(train_ds))[0]\n",
"def demo(feature_column, example_batch):\n",
" feature_layer = tf.keras.layers.DenseFeatures(feature_column)\n",
" print(feature_layer(example_batch))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Convert Origin Categorical Feature to One Hot Encoded Feature"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# only feature \n",
"v_list = tf.feature_column.categorical_column_with_vocabulary_list(\"Origin\", \n",
" train_data['Origin'].unique())\n",
"one_hot_origin_feature = tf.feature_column.indicator_column(v_list)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Example categorical field:\n",
"IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='Origin', vocabulary_list=('USA', 'Japan', 'Europe'), dtype=tf.string, default_value=-1, num_oov_buckets=0))\n",
"WARNING:tensorflow:Layer dense_features is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2. The layer has dtype float32 because it's dtype defaults to floatx.\n",
"\n",
"If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
"\n",
"To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
"\n",
"WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4267: IndicatorColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n",
"WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4322: VocabularyListCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n",
"tf.Tensor(\n",
"[[1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 0. 1.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 0. 1.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [0. 0. 1.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 0. 1.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 0. 1.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [0. 0. 1.]\n",
" [1. 0. 0.]\n",
" [0. 0. 1.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 0. 1.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 0. 1.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 0. 1.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 0. 1.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [0. 1. 0.]\n",
" [0. 0. 1.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 0. 1.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 0. 1.]\n",
" [0. 0. 1.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 0. 1.]\n",
" [0. 0. 1.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [0. 1. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]\n",
" [0. 0. 1.]\n",
" [0. 0. 1.]\n",
" [0. 0. 1.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [1. 0. 0.]\n",
" [0. 1. 0.]], shape=(128, 3), dtype=float32)\n"
]
}
],
"source": [
"print(\"Example categorical field:\\n{}\".format(one_hot_origin_feature))\n",
"demo(one_hot_origin_feature, sample_batch)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Numerical Features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NOTE: We do not use the normalizer function here because the dataset has already been normalized."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"#convert numerical features, these features have already been normalized\n",
"def create_tf_numerical_feature_cols(numerical_col_list):\n",
" tf_numeric_col_list = []\n",
" for c in numerical_col_list:\n",
" tf_numeric_feature = tf.feature_column.numeric_column(\n",
" key=c, dtype=tf.float64)\n",
" tf_numeric_col_list.append(tf_numeric_feature)\n",
" return tf_numeric_col_list"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"tf_numerical_feature_list = create_tf_numerical_feature_cols(numerical_feature_list)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[NumericColumn(key='Cylinders', shape=(1,), default_value=None, dtype=tf.float64, normalizer_fn=None),\n",
" NumericColumn(key='Displacement', shape=(1,), default_value=None, dtype=tf.float64, normalizer_fn=None),\n",
" NumericColumn(key='Horsepower', shape=(1,), default_value=None, dtype=tf.float64, normalizer_fn=None),\n",
" NumericColumn(key='Weight', shape=(1,), default_value=None, dtype=tf.float64, normalizer_fn=None),\n",
" NumericColumn(key='Acceleration', shape=(1,), default_value=None, dtype=tf.float64, normalizer_fn=None),\n",
" NumericColumn(key='ModelYear', shape=(1,), default_value=None, dtype=tf.float64, normalizer_fn=None)]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf_numerical_feature_list"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Example continuous field:\n",
"NumericColumn(key='Cylinders', shape=(1,), default_value=None, dtype=tf.float64, normalizer_fn=None)\n",
"\n",
"WARNING:tensorflow:Layer dense_features_1 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2. The layer has dtype float32 because it's dtype defaults to floatx.\n",
"\n",
"If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
"\n",
"To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
"\n",
"tf.Tensor(\n",
"[[ 0.30726957]\n",
" [ 0.30726957]\n",
" [ 1.4838872 ]\n",
" [ 0.30726957]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [ 0.30726957]\n",
" [ 1.4838872 ]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [ 1.4838872 ]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [ 1.4838872 ]\n",
" [ 1.4838872 ]\n",
" [ 1.4838872 ]\n",
" [ 1.4838872 ]\n",
" [ 1.4838872 ]\n",
" [ 0.30726957]\n",
" [ 1.4838872 ]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [ 1.4838872 ]\n",
" [ 0.30726957]\n",
" [ 1.4838872 ]\n",
" [-0.86934805]\n",
" [ 0.30726957]\n",
" [ 1.4838872 ]\n",
" [ 0.30726957]\n",
" [ 1.4838872 ]\n",
" [ 1.4838872 ]\n",
" [-0.86934805]\n",
" [ 1.4838872 ]\n",
" [-0.86934805]\n",
" [ 1.4838872 ]\n",
" [ 0.30726957]\n",
" [ 1.4838872 ]\n",
" [ 1.4838872 ]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [ 1.4838872 ]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [ 1.4838872 ]\n",
" [ 0.30726957]\n",
" [ 0.30726957]\n",
" [ 0.30726957]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [ 1.4838872 ]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [ 0.30726957]\n",
" [ 0.30726957]\n",
" [ 1.4838872 ]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [ 0.30726957]\n",
" [-1.4576569 ]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [ 1.4838872 ]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [ 0.30726957]\n",
" [ 1.4838872 ]\n",
" [ 1.4838872 ]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [ 0.30726957]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [ 1.4838872 ]\n",
" [ 1.4838872 ]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [ 0.30726957]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [ 1.4838872 ]\n",
" [-0.86934805]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [ 0.30726957]\n",
" [ 1.4838872 ]\n",
" [-0.86934805]\n",
" [ 1.4838872 ]\n",
" [ 0.30726957]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [-0.86934805]\n",
" [-0.28103924]\n",
" [ 1.4838872 ]\n",
" [ 1.4838872 ]\n",
" [ 1.4838872 ]\n",
" [-0.86934805]\n",
" [-0.86934805]], shape=(128, 1), dtype=float32)\n"
]
}
],
"source": [
"# take first numerical feature\n",
"test_cont_var1 = tf_numerical_feature_list[0]\n",
"print(\"Example continuous field:\\n{}\\n\".format(test_cont_var1))\n",
"demo(test_cont_var1, sample_batch)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Combine TF Features with DenseFeatures"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"feature_columns = [ one_hot_origin_feature ] + tf_numerical_feature_list"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='Origin', vocabulary_list=('USA', 'Japan', 'Europe'), dtype=tf.string, default_value=-1, num_oov_buckets=0)),\n",
" NumericColumn(key='Cylinders', shape=(1,), default_value=None, dtype=tf.float64, normalizer_fn=None),\n",
" NumericColumn(key='Displacement', shape=(1,), default_value=None, dtype=tf.float64, normalizer_fn=None),\n",
" NumericColumn(key='Horsepower', shape=(1,), default_value=None, dtype=tf.float64, normalizer_fn=None),\n",
" NumericColumn(key='Weight', shape=(1,), default_value=None, dtype=tf.float64, normalizer_fn=None),\n",
" NumericColumn(key='Acceleration', shape=(1,), default_value=None, dtype=tf.float64, normalizer_fn=None),\n",
" NumericColumn(key='ModelYear', shape=(1,), default_value=None, dtype=tf.float64, normalizer_fn=None)]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feature_columns"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"dense_feature_layer = tf.keras.layers.DenseFeatures(feature_columns)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"# Use same architecture as example\n",
"def build_model(dense_feature_layer):\n",
" model = tf.keras.Sequential([\n",
" dense_feature_layer,\n",
" tf.keras.layers.Dense(64, activation='relu'),\n",
" tf.keras.layers.Dense(64, activation='relu'),\n",
" tf.keras.layers.Dense(1)\n",
" ])\n",
"\n",
" optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
"\n",
" model.compile(loss='mse',\n",
" optimizer=optimizer,\n",
" metrics=['mae', 'mse'])\n",
" \n",
"\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"model = build_model(dense_feature_layer)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# validation split"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train for 3 steps\n",
"Epoch 1/2000\n",
"3/3 [==============================] - 1s 199ms/step - loss: 585.3821 - mae: 23.0968 - mse: 594.5055\n",
"Epoch 2/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 574.6058 - mae: 22.5887 - mse: 571.0825\n",
"Epoch 3/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 545.8865 - mae: 22.1524 - mse: 550.8449\n",
"Epoch 4/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 530.4553 - mae: 21.7258 - mse: 531.0057\n",
"Epoch 5/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 503.8924 - mae: 21.2703 - mse: 510.2128\n",
"Epoch 6/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 496.5905 - mae: 20.7890 - mse: 488.6298\n",
"Epoch 7/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 455.9064 - mae: 20.2625 - mse: 465.7529\n",
"Epoch 8/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 443.9649 - mae: 19.7280 - mse: 443.1337\n",
"Epoch 9/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 425.1274 - mae: 19.1525 - mse: 418.9799\n",
"Epoch 10/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 400.7790 - mae: 18.5370 - mse: 394.0287\n",
"Epoch 11/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 369.3888 - mae: 17.8949 - mse: 368.7041\n",
"Epoch 12/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 342.8452 - mae: 17.2311 - mse: 343.5887\n",
"Epoch 13/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 315.7890 - mae: 16.5375 - mse: 318.3971\n",
"Epoch 14/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 294.0952 - mae: 15.8151 - mse: 292.9583\n",
"Epoch 15/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 266.5711 - mae: 15.0530 - mse: 267.3140\n",
"Epoch 16/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 240.9918 - mae: 14.2604 - mse: 242.1907\n",
"Epoch 17/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 216.5080 - mae: 13.4417 - mse: 217.2129\n",
"Epoch 18/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 194.5078 - mae: 12.5964 - mse: 192.9444\n",
"Epoch 19/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 169.6571 - mae: 11.7405 - mse: 169.3144\n",
"Epoch 20/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 143.4581 - mae: 10.8984 - mse: 147.5415\n",
"Epoch 21/2000\n",
"3/3 [==============================] - ETA: 0s - loss: 135.8141 - mae: 10.4524 - mse: 135.81 - 0s 8ms/step - loss: 125.3692 - mae: 10.0776 - mse: 127.5220\n",
"Epoch 22/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 109.8136 - mae: 9.2492 - mse: 108.6243\n",
"Epoch 23/2000\n",
"3/3 [==============================] - ETA: 0s - loss: 92.8122 - mae: 8.4186 - mse: 92.812 - 0s 8ms/step - loss: 90.7153 - mae: 8.4431 - mse: 91.6103\n",
"Epoch 24/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 77.3159 - mae: 7.6655 - mse: 76.9331\n",
"Epoch 25/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 63.2884 - mae: 6.9069 - mse: 63.8797\n",
"Epoch 26/2000\n",
"3/3 [==============================] - ETA: 0s - loss: 54.7122 - mae: 6.3390 - mse: 54.712 - 0s 8ms/step - loss: 51.0899 - mae: 6.2350 - mse: 53.3833\n",
"Epoch 27/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 43.8263 - mae: 5.6502 - mse: 45.0143\n",
"Epoch 28/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 38.4137 - mae: 5.1546 - mse: 38.5296\n",
"Epoch 29/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 34.2481 - mae: 4.7481 - mse: 33.4797\n",
"Epoch 30/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 29.7677 - mae: 4.3981 - mse: 29.5008\n",
"Epoch 31/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 27.3373 - mae: 4.1821 - mse: 26.9773\n",
"Epoch 32/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 24.9477 - mae: 4.0365 - mse: 25.1653\n",
"Epoch 33/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 23.4353 - mae: 3.9030 - mse: 23.6679\n",
"Epoch 34/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 22.9886 - mae: 3.7960 - mse: 22.4155\n",
"Epoch 35/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 21.8989 - mae: 3.6914 - mse: 21.3217\n",
"Epoch 36/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 20.5761 - mae: 3.5858 - mse: 20.2436\n",
"Epoch 37/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 19.5548 - mae: 3.4930 - mse: 19.2599\n",
"Epoch 38/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 18.2483 - mae: 3.3847 - mse: 18.2140\n",
"Epoch 39/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 17.3819 - mae: 3.3041 - mse: 17.3903\n",
"Epoch 40/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 15.8858 - mae: 3.2119 - mse: 16.4883\n",
"Epoch 41/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 15.3103 - mae: 3.1251 - mse: 15.6404\n",
"Epoch 42/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 15.0518 - mae: 3.0429 - mse: 14.8928\n",
"Epoch 43/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 13.7830 - mae: 2.9691 - mse: 14.2292\n",
"Epoch 44/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 13.8347 - mae: 2.8899 - mse: 13.6046\n",
"Epoch 45/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 12.8786 - mae: 2.8209 - mse: 12.9365\n",
"Epoch 46/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 12.0197 - mae: 2.7410 - mse: 12.3174\n",
"Epoch 47/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 11.4542 - mae: 2.6740 - mse: 11.8457\n",
"Epoch 48/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 10.7790 - mae: 2.6058 - mse: 11.3042\n",
"Epoch 49/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 11.2904 - mae: 2.5530 - mse: 10.9965\n",
"Epoch 50/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 11.5568 - mae: 2.4975 - mse: 10.6270\n",
"Epoch 51/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 9.7172 - mae: 2.4393 - mse: 10.1553\n",
"Epoch 52/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 9.8445 - mae: 2.3767 - mse: 9.8364\n",
"Epoch 53/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 9.4953 - mae: 2.3313 - mse: 9.5531\n",
"Epoch 54/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 9.4217 - mae: 2.3044 - mse: 9.4062\n",
"Epoch 55/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 8.9688 - mae: 2.2650 - mse: 9.2298\n",
"Epoch 56/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 9.2789 - mae: 2.2440 - mse: 9.0522\n",
"Epoch 57/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 9.4226 - mae: 2.2652 - mse: 9.5130\n",
"Epoch 58/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 9.0158 - mae: 2.1746 - mse: 8.6252\n",
"Epoch 59/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 8.9102 - mae: 2.1745 - mse: 8.6284\n",
"Epoch 60/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 8.4428 - mae: 2.1725 - mse: 8.7781\n",
"Epoch 61/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 8.5132 - mae: 2.1343 - mse: 8.3563\n",
"Epoch 62/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 8.5507 - mae: 2.1092 - mse: 8.2538\n",
"Epoch 63/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 8.4070 - mae: 2.0932 - mse: 8.2067\n",
"Epoch 64/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 8.1978 - mae: 2.0672 - mse: 8.0786\n",
"Epoch 65/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 7.9472 - mae: 2.0544 - mse: 7.9738\n",
"Epoch 66/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 8.0614 - mae: 2.0647 - mse: 7.9625\n",
"Epoch 67/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 8.2943 - mae: 2.0271 - mse: 7.9666\n",
"Epoch 68/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 7.6710 - mae: 2.0433 - mse: 7.8157\n",
"Epoch 69/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 7.9604 - mae: 2.0203 - mse: 7.6802\n",
"Epoch 70/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.7547 - mae: 2.0114 - mse: 7.7436\n",
"Epoch 71/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.7538 - mae: 2.0133 - mse: 7.7127\n",
"Epoch 72/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 8.0305 - mae: 2.0101 - mse: 7.6614\n",
"Epoch 73/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 7.3242 - mae: 1.9727 - mse: 7.4794\n",
"Epoch 74/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.2032 - mae: 2.0033 - mse: 7.5519\n",
"Epoch 75/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.9128 - mae: 1.9798 - mse: 7.5000\n",
"Epoch 76/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 8.0712 - mae: 1.9744 - mse: 7.4833\n",
"Epoch 77/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.6811 - mae: 1.9756 - mse: 7.4861\n",
"Epoch 78/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.5958 - mae: 1.9613 - mse: 7.2936\n",
"Epoch 79/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.4479 - mae: 1.9429 - mse: 7.2497\n",
"Epoch 80/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 7.1300 - mae: 1.9531 - mse: 7.2651\n",
"Epoch 81/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.1703 - mae: 1.9365 - mse: 7.1995\n",
"Epoch 82/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.5269 - mae: 1.9649 - mse: 7.2307\n",
"Epoch 83/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.0747 - mae: 1.9255 - mse: 7.0952\n",
"Epoch 84/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.2697 - mae: 1.9504 - mse: 7.2385\n",
"Epoch 85/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 6.7345 - mae: 1.9004 - mse: 7.0723\n",
"Epoch 86/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.8089 - mae: 1.9562 - mse: 7.2450\n",
"Epoch 87/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.8581 - mae: 1.9540 - mse: 7.1739\n",
"Epoch 88/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.3251 - mae: 1.9207 - mse: 7.0819\n",
"Epoch 89/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.1417 - mae: 1.9316 - mse: 7.3287\n",
"Epoch 90/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.9482 - mae: 1.9147 - mse: 7.0760\n",
"Epoch 91/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.9482 - mae: 1.9067 - mse: 6.9849\n",
"Epoch 92/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.6852 - mae: 1.9425 - mse: 7.3410\n",
"Epoch 93/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 7.0067 - mae: 1.9155 - mse: 7.0058\n",
"Epoch 94/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.6812 - mae: 1.8983 - mse: 6.9463\n",
"Epoch 95/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.0309 - mae: 1.8912 - mse: 6.9202\n",
"Epoch 96/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.8703 - mae: 1.8856 - mse: 6.8720\n",
"Epoch 97/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.8351 - mae: 1.8838 - mse: 6.8540\n",
"Epoch 98/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 7.1849 - mae: 1.8881 - mse: 6.9856\n",
"Epoch 99/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 7.3012 - mae: 1.8810 - mse: 6.8760\n",
"Epoch 100/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.9392 - mae: 1.8860 - mse: 6.8830\n",
"Epoch 101/2000\n",
"3/3 [==============================] - 0s 12ms/step - loss: 7.0547 - mae: 1.9027 - mse: 6.9497\n",
"Epoch 102/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 7.1103 - mae: 1.8934 - mse: 6.8656\n",
"Epoch 103/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.9369 - mae: 1.8769 - mse: 6.9223\n",
"Epoch 104/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.7814 - mae: 1.8953 - mse: 6.9959\n",
"Epoch 105/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.7724 - mae: 1.8566 - mse: 6.7088\n",
"Epoch 106/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 6.5848 - mae: 1.8611 - mse: 6.7436\n",
"Epoch 107/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.4694 - mae: 1.8862 - mse: 6.7859\n",
"Epoch 108/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.5204 - mae: 1.8635 - mse: 6.8004\n",
"Epoch 109/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.1073 - mae: 1.8802 - mse: 6.7803\n",
"Epoch 110/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.6102 - mae: 1.8712 - mse: 6.7075\n",
"Epoch 111/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 7.0358 - mae: 1.8641 - mse: 6.7502\n",
"Epoch 112/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.4283 - mae: 1.8558 - mse: 6.6167\n",
"Epoch 113/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.7455 - mae: 1.8403 - mse: 6.5516\n",
"Epoch 114/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.9360 - mae: 1.8447 - mse: 6.7677\n",
"Epoch 115/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.9014 - mae: 1.9038 - mse: 7.0244\n",
"Epoch 116/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.5749 - mae: 1.8568 - mse: 6.7466\n",
"Epoch 117/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.9168 - mae: 1.8410 - mse: 6.5929\n",
"Epoch 118/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.3338 - mae: 1.8329 - mse: 6.6516\n",
"Epoch 119/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.3857 - mae: 1.8451 - mse: 6.5571\n",
"Epoch 120/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.5670 - mae: 1.8403 - mse: 6.6343\n",
"Epoch 121/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.3706 - mae: 1.8155 - mse: 6.5242\n",
"Epoch 122/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.3550 - mae: 1.8280 - mse: 6.4625\n",
"Epoch 123/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 7.1061 - mae: 1.8600 - mse: 6.8148\n",
"Epoch 124/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.9283 - mae: 1.8144 - mse: 6.4510\n",
"Epoch 125/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.6522 - mae: 1.8161 - mse: 6.4268\n",
"Epoch 126/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.4479 - mae: 1.8152 - mse: 6.4432\n",
"Epoch 127/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.7081 - mae: 1.8422 - mse: 6.5147\n",
"Epoch 128/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.5851 - mae: 1.8199 - mse: 6.4477\n",
"Epoch 129/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.2978 - mae: 1.8190 - mse: 6.4157\n",
"Epoch 130/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.6583 - mae: 1.8203 - mse: 6.5203\n",
"Epoch 131/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.0811 - mae: 1.8208 - mse: 6.3977\n",
"Epoch 132/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.5420 - mae: 1.8173 - mse: 6.4045\n",
"Epoch 133/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.5767 - mae: 1.8140 - mse: 6.5334\n",
"Epoch 134/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.5169 - mae: 1.8300 - mse: 6.6346\n",
"Epoch 135/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.5092 - mae: 1.8297 - mse: 6.4882\n",
"Epoch 136/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.6308 - mae: 1.8452 - mse: 6.6794\n",
"Epoch 137/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.4223 - mae: 1.8118 - mse: 6.4209\n",
"Epoch 138/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.9083 - mae: 1.8077 - mse: 6.3931\n",
"Epoch 139/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.2590 - mae: 1.7878 - mse: 6.3605\n",
"Epoch 140/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.0401 - mae: 1.7884 - mse: 6.3866\n",
"Epoch 141/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.3193 - mae: 1.7855 - mse: 6.2789\n",
"Epoch 142/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 7.0180 - mae: 1.7822 - mse: 6.2714\n",
"Epoch 143/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.6354 - mae: 1.8433 - mse: 6.4764\n",
"Epoch 144/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.4707 - mae: 1.7890 - mse: 6.2760\n",
"Epoch 145/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 6.5675 - mae: 1.7960 - mse: 6.4465\n",
"Epoch 146/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.0615 - mae: 1.7964 - mse: 6.2981\n",
"Epoch 147/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.2501 - mae: 1.8102 - mse: 6.3750\n",
"Epoch 148/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.4839 - mae: 1.7919 - mse: 6.4012\n",
"Epoch 149/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.1457 - mae: 1.7866 - mse: 6.2010\n",
"Epoch 150/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.0410 - mae: 1.7875 - mse: 6.3815\n",
"Epoch 151/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.1925 - mae: 1.7895 - mse: 6.2990\n",
"Epoch 152/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.5071 - mae: 1.7646 - mse: 6.1655\n",
"Epoch 153/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.0075 - mae: 1.7724 - mse: 6.3797\n",
"Epoch 154/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.8193 - mae: 1.7660 - mse: 6.1626\n",
"Epoch 155/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.9418 - mae: 1.7723 - mse: 6.2764\n",
"Epoch 156/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.1433 - mae: 1.7685 - mse: 6.1648\n",
"Epoch 157/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.1104 - mae: 1.7679 - mse: 6.3274\n",
"Epoch 158/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.7620 - mae: 1.7673 - mse: 6.1164\n",
"Epoch 159/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.0829 - mae: 1.7818 - mse: 6.1666\n",
"Epoch 160/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.4117 - mae: 1.7591 - mse: 6.1142\n",
"Epoch 161/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.4644 - mae: 1.7715 - mse: 6.2041\n",
"Epoch 162/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.9909 - mae: 1.7907 - mse: 6.2320\n",
"Epoch 163/2000\n",
"3/3 [==============================] - 0s 11ms/step - loss: 6.1793 - mae: 1.7825 - mse: 6.2296\n",
"Epoch 164/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.9873 - mae: 1.7523 - mse: 6.1457\n",
"Epoch 165/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 6.2946 - mae: 1.7799 - mse: 6.2261\n",
"Epoch 166/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.2775 - mae: 1.7831 - mse: 6.2734\n",
"Epoch 167/2000\n",
"3/3 [==============================] - 0s 8ms/step - loss: 6.1816 - mae: 1.7438 - mse: 6.0463\n",
"Epoch 168/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.0167 - mae: 1.7617 - mse: 6.1526\n",
"Epoch 169/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.4572 - mae: 1.7648 - mse: 6.1620\n",
"Epoch 170/2000\n",
"3/3 [==============================] - ETA: 0s - loss: 4.7807 - mae: 1.5834 - mse: 4.780 - 0s 10ms/step - loss: 6.1761 - mae: 1.7664 - mse: 6.1549\n",
"Epoch 171/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.1865 - mae: 1.7833 - mse: 6.2049\n",
"Epoch 172/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.8784 - mae: 1.7647 - mse: 6.1704\n",
"Epoch 173/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.2366 - mae: 1.7535 - mse: 6.1110\n",
"Epoch 174/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.0146 - mae: 1.7875 - mse: 6.2664\n",
"Epoch 175/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.1762 - mae: 1.7420 - mse: 5.9928\n",
"Epoch 176/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.6635 - mae: 1.7789 - mse: 6.3199\n",
"Epoch 177/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.3631 - mae: 1.7628 - mse: 6.0772\n",
"Epoch 178/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.0742 - mae: 1.7460 - mse: 6.0985\n",
"Epoch 179/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.1650 - mae: 1.7658 - mse: 6.0822\n",
"Epoch 180/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.1875 - mae: 1.7366 - mse: 5.9497\n",
"Epoch 181/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.2169 - mae: 1.7534 - mse: 5.9918\n",
"Epoch 182/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.6478 - mae: 1.7546 - mse: 6.1283\n",
"Epoch 183/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.7900 - mae: 1.7595 - mse: 6.0475\n",
"Epoch 184/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.8844 - mae: 1.7637 - mse: 6.1431\n",
"Epoch 185/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.1660 - mae: 1.7490 - mse: 5.9714\n",
"Epoch 186/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.1022 - mae: 1.7434 - mse: 5.9563\n",
"Epoch 187/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.8924 - mae: 1.7297 - mse: 5.8981\n",
"Epoch 188/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.2900 - mae: 1.7343 - mse: 5.9562\n",
"Epoch 189/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.5532 - mae: 1.7896 - mse: 6.2489\n",
"Epoch 190/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.8688 - mae: 1.7676 - mse: 5.9838\n",
"Epoch 191/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.6497 - mae: 1.7255 - mse: 5.9006\n",
"Epoch 192/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.9428 - mae: 1.7233 - mse: 5.9542\n",
"Epoch 193/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.8974 - mae: 1.7222 - mse: 5.9369\n",
"Epoch 194/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.9112 - mae: 1.7351 - mse: 5.9096\n",
"Epoch 195/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.9357 - mae: 1.7279 - mse: 5.8833\n",
"Epoch 196/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.2138 - mae: 1.7560 - mse: 6.0180\n",
"Epoch 197/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.9198 - mae: 1.7283 - mse: 5.9794\n",
"Epoch 198/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.9006 - mae: 1.7275 - mse: 5.9857\n",
"Epoch 199/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.0051 - mae: 1.7328 - mse: 6.0904\n",
"Epoch 200/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.8725 - mae: 1.7351 - mse: 5.9403\n",
"Epoch 201/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.0794 - mae: 1.7040 - mse: 5.8176\n",
"Epoch 202/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.7613 - mae: 1.7200 - mse: 5.8300\n",
"Epoch 203/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.4408 - mae: 1.7787 - mse: 6.2403\n",
"Epoch 204/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.9808 - mae: 1.7114 - mse: 5.9193\n",
"Epoch 205/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.9880 - mae: 1.7138 - mse: 5.8420\n",
"Epoch 206/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.5858 - mae: 1.7130 - mse: 5.8880\n",
"Epoch 207/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.7301 - mae: 1.7344 - mse: 5.9319\n",
"Epoch 208/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.1862 - mae: 1.7283 - mse: 6.0043\n",
"Epoch 209/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.5220 - mae: 1.7293 - mse: 5.9897\n",
"Epoch 210/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.7023 - mae: 1.7106 - mse: 5.8372\n",
"Epoch 211/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.9678 - mae: 1.7191 - mse: 5.8035\n",
"Epoch 212/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.8807 - mae: 1.6991 - mse: 5.7652\n",
"Epoch 213/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.7437 - mae: 1.7258 - mse: 5.8507\n",
"Epoch 214/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.8151 - mae: 1.7220 - mse: 6.1433\n",
"Epoch 215/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.2716 - mae: 1.7241 - mse: 5.9999\n",
"Epoch 216/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.8397 - mae: 1.7134 - mse: 5.8159\n",
"Epoch 217/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.6889 - mae: 1.7072 - mse: 5.8050\n",
"Epoch 218/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.2386 - mae: 1.7389 - mse: 5.9700\n",
"Epoch 219/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.6640 - mae: 1.7172 - mse: 5.9544\n",
"Epoch 220/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.0570 - mae: 1.7050 - mse: 5.7955\n",
"Epoch 221/2000\n",
"3/3 [==============================] - 0s 10ms/step - loss: 6.0698 - mae: 1.7120 - mse: 5.7713\n",
"Epoch 222/2000\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.7421 - mae: 1.6913 - mse: 5.7704\n"
]
}
],
"source": [
"# No validation set b/c need to build separate TF dataset\n",
"EPOCHS = 2000\n",
"# Set to patience to 100 so it trains to end of epochs\n",
"early_stop = tf.keras.callbacks.EarlyStopping(monitor='mse', patience=10) \n",
"history = model.fit(train_ds, callbacks=[early_stop], epochs=EPOCHS, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1/1 - 0s - loss: 5.9397 - mae: 1.9018 - mse: 5.9397\n"
]
}
],
"source": [
"loss, mae, mse = model.evaluate(test_ds, verbose=2)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"test_labels = test_data[PREDICTOR_FIELD].values"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"test_predictions = model.predict(test_ds).flatten()\n",
"\n",
"a = plt.axes(aspect='equal')\n",
"plt.scatter(test_labels, test_predictions)\n",
"plt.xlabel('True Values [MPG]')\n",
"plt.ylabel('Predictions [MPG]')\n",
"lims = [0, 50]\n",
"plt.xlim(lims)\n",
"plt.ylim(lims)\n",
"_ = plt.plot(lims, lims)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show Model Prediction Output"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"model_pred_outputs = {\n",
" \"pred\": test_predictions,\n",
" \"actual_value\": test_labels,\n",
"}\n",
"model_output_df = pd.DataFrame(model_pred_outputs)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pred</th>\n",
" <th>actual_value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20.839663</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>30.319689</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>18.583944</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>30.072401</td>\n",
" <td>25.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>32.988171</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pred actual_value\n",
"0 20.839663 15.0\n",
"1 30.319689 10.0\n",
"2 18.583944 9.0\n",
"3 30.072401 25.0\n",
"4 32.988171 19.0"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_output_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Model Evaluation "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Preparing Regression Output for Binary Scoring Model Evaluation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Create binary label for mileage prediction over 25 miles per gallon\n",
" - 0 = Not Fuel Efficient, < 25 MPG\n",
" - 1 = Fuel Efficient >= 25 MPG"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pred</th>\n",
" <th>actual_value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20.839663</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>30.319689</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>18.583944</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>30.072401</td>\n",
" <td>25.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>32.988171</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pred actual_value\n",
"0 20.839663 15.0\n",
"1 30.319689 10.0\n",
"2 18.583944 9.0\n",
"3 30.072401 25.0\n",
"4 32.988171 19.0"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert Regression Output to binary classification output\n",
"model_output_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"def convert_to_binary(df, pred_field, actual_field):\n",
" df['score'] = df[pred_field].apply(lambda x: 1 if x>=25 else 0 )\n",
" df['label_value'] = df[actual_field].apply(lambda x: 1 if x>=25 else 0)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pred</th>\n",
" <th>actual_value</th>\n",
" <th>score</th>\n",
" <th>label_value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20.839663</td>\n",
" <td>15.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>30.319689</td>\n",
" <td>10.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>18.583944</td>\n",
" <td>9.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>30.072401</td>\n",
" <td>25.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>32.988171</td>\n",
" <td>19.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pred actual_value score label_value\n",
"0 20.839663 15.0 0 0\n",
"1 30.319689 10.0 1 0\n",
"2 18.583944 9.0 0 0\n",
"3 30.072401 25.0 1 1\n",
"4 32.988171 19.0 1 0"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"binary_df = convert_to_binary(model_output_df, 'pred', 'actual_value')\n",
"binary_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Classification Common Model Evaluation Metrics"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import brier_score_loss, accuracy_score, f1_score, classification_report, roc_auc_score, roc_curve"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.51 0.54 0.52 41\n",
" 1 0.46 0.43 0.44 37\n",
"\n",
" accuracy 0.49 78\n",
" macro avg 0.48 0.48 0.48 78\n",
"weighted avg 0.49 0.49 0.49 78\n",
"\n"
]
}
],
"source": [
"print(classification_report(binary_df['label_value'], binary_df['score']))"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.4861619861619862"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#F1\n",
"f1_score(binary_df['label_value'], binary_df['score'], average='weighted')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.4845088991430455"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#AUC - area under the ROC curve\n",
"roc_auc_score(binary_df['label_value'], binary_df['score'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Brier Score"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.63984055, 0.7273677 , 0.08202658, 0.10802052, 0.90688995,\n",
" 0.59708992, 0.17010786, 0.12262666, 0.20686824, 0.48466274,\n",
" 0.21875581, 0.82377467, 0.53842718, 0.24924377, 0.32144547,\n",
" 0.72764235, 0.91161052, 0.24321754, 0.50343573, 0.92028677,\n",
" 0.74997289, 0.12646407, 0.98721181, 0.17021378, 0.56310876,\n",
" 0.85966708, 0.62865988, 0.33722862, 0.62492099, 0.40815499,\n",
" 0.65274542, 0.30883971, 0.01071058, 0.8678821 , 0.12334513,\n",
" 0.40803165, 0.74179083, 0.91860733, 0.2814972 , 0.5356407 ,\n",
" 0.41105764, 0.33681037, 0.1811007 , 0.16204914, 0.09028951,\n",
" 0.27544439, 0.82239872, 0.0450141 , 0.06412615, 0.85322593,\n",
" 0.91366681, 0.67436885, 0.90712883, 0.27643937, 0.88069547,\n",
" 0.45884956, 0.04134214, 0.73975312, 0.72330834, 0.44275507,\n",
" 0.71376937, 0.69853622, 0.00582107, 0.84070373, 0.00988595,\n",
" 0.34868655, 0.59928363, 0.1452982 , 0.09137412, 0.56005994,\n",
" 0.95636292, 0.72252451, 0.05760047, 0.02081183, 0.82771759,\n",
" 0.01368662, 0.63151811, 0.9429718 , 0.49583587, 0.07421305,\n",
" 0.13772224, 0.7149784 , 0.86850646, 0.80028487, 0.29937771,\n",
" 0.10234995, 0.50740118, 0.29525266, 0.51646542, 0.41637914,\n",
" 0.53578371, 0.36553001, 0.87027022, 0.07157494, 0.23841116,\n",
" 0.40403308, 0.96537963, 0.6482574 , 0.51317208, 0.63067597])"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"random_predictions = np.random.random((100))\n",
"random_predictions"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n",
" 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1,\n",
" 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1,\n",
" 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1])"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"random_labels = np.random.choice([0, 1], 100)\n",
"random_labels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Convert probability to binary predictions"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"probability_prediction_df = pd.DataFrame({\"prob_prediction\": random_predictions,\n",
" \"binary_prediction\": np.array([ 1 if abs(x)>=0.5 else 0 for x in random_predictions], dtype=int),\n",
" \"label\": random_labels})"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>prob_prediction</th>\n",
" <th>binary_prediction</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.639841</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.727368</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.082027</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.108021</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.906890</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" prob_prediction binary_prediction label\n",
"0 0.639841 1 0\n",
"1 0.727368 1 1\n",
"2 0.082027 0 1\n",
"3 0.108021 0 0\n",
"4 0.906890 1 0"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"probability_prediction_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"y_true = probability_prediction_df['label'].values \n",
"y_pred = probability_prediction_df['binary_prediction'].values"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.53\n"
]
}
],
"source": [
"acc = accuracy_score(y_true, y_pred)\n",
"print(acc)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.321911857133992\n"
]
}
],
"source": [
"bs_loss = brier_score_loss(y_true, probability_prediction_df['prob_prediction'])\n",
"print(bs_loss)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Misclassified"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"missclassified_df = probability_prediction_df[probability_prediction_df['binary_prediction']!=probability_prediction_df['label']]"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f9c0571b150>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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wwXG4HwmcluTOqrpoMiU2MfS1fmtV3Q7cnuRS4DhgMwf8kH6fBZxbo4np65N8BXgEcMVkSpyaNWfbRp+iGbLcwUeA54/fcX488L9V9c1JF9rAsn1PMgtcADxvk4/iFlu231X10Kqaq6o54HzgRZs83GHYa/3vgScl2ZLkMEars+6ccJ3rbUi/v8bofy0keQBwDHDDRKucjjVn24YewddeljtI8gfj429ldBfFacD1wPcZ/Wu/6Q3s+yuB+wFvGY9m76xNvvLewH53Z0i/q2pnkk8AVwE/Bt5RVUveYrdZDPx9/znwriRXM5q2OKeqNv0Swkk+ADwFODLJTcCrgINg/bLNpQokqVMbfYpGkrRKBrwkdcqAl6ROGfCS1CkDXpI6ZcBLUqcMeEnq1P8BPq7pEQ93iaYAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"missclassified_df['prob_prediction'].plot(kind='hist')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Demographic Group Bias Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Select groups to analyze\n",
"- Prepare data \n",
"- Analyze groups\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Select groups to analyze\n",
"- Selected 'origin' field which gives the country of origin for the car."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"#combine binary df with original test df, order was not shuffled so can do"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"merged_binary_df = binary_df.assign(origin = test_data['Origin'].values)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>pred</th>\n",
" <th>actual_value</th>\n",
" <th>score</th>\n",
" <th>label_value</th>\n",
" <th>origin</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20.839663</td>\n",
" <td>15.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>30.319689</td>\n",
" <td>10.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>18.583944</td>\n",
" <td>9.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>30.072401</td>\n",
" <td>25.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Japan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>32.988171</td>\n",
" <td>19.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>USA</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pred actual_value score label_value origin\n",
"0 20.839663 15.0 0 0 USA\n",
"1 30.319689 10.0 1 0 USA\n",
"2 18.583944 9.0 0 0 USA\n",
"3 30.072401 25.0 1 1 Japan\n",
"4 32.988171 19.0 1 0 USA"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_binary_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare Data"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"# Aequitas\n",
"from aequitas.preprocessing import preprocess_input_df\n",
"from aequitas.group import Group\n",
"from aequitas.plotting import Plot\n",
"from aequitas.bias import Bias\n",
"from aequitas.fairness import Fairness"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"model_id, score_thresholds 1 {'rank_abs': [35]}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.7/site-packages/aequitas/group.py:30: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead\n",
" divide = lambda x, y: x / y if y != 0 else pd.np.nan\n"
]
}
],
"source": [
"ae_df, _ = preprocess_input_df(merged_binary_df)\n",
"g = Group()\n",
"xtab, _ = g.get_crosstabs(ae_df)\n",
"absolute_metrics = g.list_absolute_metrics(xtab)\n",
"clean_xtab = xtab.fillna(-1)\n",
"aqp = Plot()\n",
"b = Bias()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Analyze Groups"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
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iV+PLp3LXs58D8OH3PzL6Hx/XeOwiIiJy7KkgIyIiUneZ/08XoK94294jmCvQHJXJCzBHWHzvse9AHlPfW8K4i/oAENcgmonXnsasey4sM/bOS/qRMu1GUqbdyPdPXkf96EhGnn0iAEMHdiJ5XQYbtu6p0fhFgpGZmcnw4cOJjY2lffv2zJ49u9JzhgwZgpmVeMJMMPMItG7duvJBUmspf+FJT1kSERGpu9YDh4DBwI8AZhYJnApU5SecNcAg4MVibYOOUozHzN/e/p45X6whv8BRPzqS6eN+QZ/OvgU6NvQJHhp5Kh//byO7svfz2I1ncPngJACunfwBP6RncjCvgC5tmvDC7y6gaaP6zF+Wxu+e/ZyTu7Xmm9VbMDPm/PEiTmzXvMy15y5Yy5k929Igph4A8bExnNnreOYvS6sw5vcXbSCxaSwDkhKL2q46I4kXPl7On28842h9NCLHxLhx44iOjiYjI4OUlBQuvPBCevfuTY8ePQKOnzVrVsBH/VZ1HvHRY5O9TfkLT2HxWyoREZFw5JzbC0wHJvmfpHSi/7gV8FQVppoCjDKzm8ysq5n9ATiZ4FbN1Jgbzu3B91Ou53/TbuCR6wdz27RPSvRHRBgL/zaS9x4azq3/+Jjte3wLhaaMPofkqdezfPooerRrweNv/Xy31sqNu7htaG+WPTWKq87oxqNzvg147fnLN3Fyt6r/tvOFj1dw0y97lWg79YQ2/Del4kKOSKjt3buXuXPn8sgjjxAXF8fgwYMZNmwYM2fODDg+KyuLiRMnMnny5GrNIz/TprDepvyFJ62QERERqdv+6P/zRaAJ8D/gAufcVjPrdiQTOOfmmFknYBLQEHgbeBq45BjEe9QsXpfBY68vIjN3PxFmrN28u0T/zf7CR7e2zejXuRXfrtnKsFO68Mp/VzFr/moO5RWw92AeScc1LTqnW9um9O3cCoBTTmjN+4s2BLx2+s5cLhoUW6V4t2bm8tmyNF6664IS7YlNY0nfmVOluURq2tq1a4mMjCQpKamorXfv3nzxxRcBx48fP54xY8aQmJhYor2q84iIeJkKMiIiInWYc+4g8Dv/q3TffH7eZ6Z4+0vAS6XaHgMeO3xsZv/Cd0vU4f4JwIRix6MCzFtizLFU6OCKx97jy8lX069LK7bsyuW4658ud7zDYWZ8tSKd6R+msPBvI0mIb8jsz1fz7EdLi8bVr/fzt06REUZ+QWHA+RpER3HgUNWWn7/86UqGDuhIi/iGJdoP5BXQIEbfskntlpubS3x8fIm2+Ph4cnLKFhOTk5NZsGABU6ZMIT09Peh5pKSmTZtWPkhqLeUvPOmWJREREamQmTU0s9+bWQ8z62Zm4/Gtjnkh1LFVJL+gkOMTGgHw1L9TyvS/+MkKANZt3k3Kjzs4uVsie/YeJL5hDM0bNeBgXj4vfLI8qGv36tCCH9Izq3TOS5+uLHO7EsDqtF307pgQVBwiNSUuLo7s7OwSbdnZ2TRq1KhEW2FhIWPHjmXKlClERZUtNB7pPFJWXFxcqEOQalD+wpMKMiIiIlIZB/wK+BLfLU8jgOudc/8KaVTlyC8opGFMFA9fdzoDf/sqZ94zh9j69cqMi6kXyem/n81FE97mmdt/QcsmsfxqQEc6t27CCbe+wK8emEs//+1JVXXZ6V2ZtyS16LigoJC21z/NlY+9z7LUHbS9/mkmvFr0NHIWrNxMzv5DnN+vQ5m55i1J5fLTk8q0i9QmSUlJ5Ofns27duqK2pUuXltmINzs7m+TkZEaMGEFiYiIDBw4EoG3btnz11VdHPI+UtWnTplCHINWg/IUnc65W78cnIiIiYWZA10SXPPX6oM7dmpnLCbe+wLZZY4qecBSIDX2CnLl3EtcgOtgwK3X+/W8x6ddnFO05E4xd2fsZct8bfP/kdUTXizyK0YmU41d/DfrUq6++GjNjxowZpKSkMHToUBYuXFiimOKcIyMjo+h406ZNDBo0iPT0dBISEoiOjj6ieaSs9evX06VLl1CHIUFS/rzLzBY75wYEc65WyIiIiEjtYsF9ezL13SWcc+8bPPGbsyssxtSUaWPOZWvm3mrN8eO2PUwfd56KMVIjCqIaVj6oAk899RT79++nZcuWXHPNNUyfPp0ePXqQlpZGXFwcaWlpmBmJiYlFr4QE3+14rVq1Ijo6usJ5pGL169cPdQhSDcpfeNIKGREREalVBgwY4JKTk0MdhoiIiEiltEJGRERE6oyDBw+GOgSphh9//DHUIUiQlDtvU/68TfkLTyrIiIiIiMhRU1gY+FHgUvspd96m/Hmb8heeyj5rTkRERCSEovN3w7cTQh2GBKkLwM5QRyHBqHbu6sVC/3uOUjQiInWfVsiIiIhIrWLot4QinpRXvU2spXo6d+4c6hCkGpS/8KSCjIiIiIiIiMdt27Yt1CFINSh/4UkFGREREREREY/bu1crlLxM+QtPKsiIiIiIiIiIiNQwFWREREREREQ8rk2bNqEOQapB+QtPesqSiIiI1Bkdhj9J/ego6sf8/C3OO49fTYfWTUIYVWDvfrmGT777kWl3D+XgoXwu+cMcktdsAWDnR38oGrdw2Sbu/sfH7Mk9AMCFp3Xl8XG/ICLCWLFhO3/45yd8+PdrQ/IeRKT2OHjwIA0bNgx1GBIk5S88aYWMiIiI1ClvPXYVKa/cVvSqajEmP79mnvJ0/zOfc+8NgwGIjIjg7pGn8enUG8qMaxwbw8sPXsqq18bxv5dH882KdGbNWwZAz84tia4XyeeLf6qRmEWOpczMTIYPH05sbCzt27dn9uzZlZ4zZMgQzIz8/Pyituuuu47WrVvTuHFjkpKSmDFjxrEMu9bYtWtXqEOQalD+wpMKMiIiIlLnpW7dQ4sLJgc8Pvz1hBnzGTz6BWa8t4T1mzI59/ZXOOm66fS78Rk++mZ90bl26kQmzJjPabc8T7cR05j7+aqivkUr0zln3Mv0H/Us/Uc9ywcL1gaM56uUjbRo0pC2LRsDEBUVwXmDOtGkUf0yY3t2bknX45sDEBMdRd+kRDZuyyrqv+YXPZnx3v+q8emI1A7jxo0jOjqajIwMZs2axZgxY1i5cmW542fNmlWiEHPYfffdR2pqKtnZ2bz33nvcf//9LF68+FiGLiISFN2yJCIiInXKFePfKLplKSoyguQXb630nF1Z+zmxQwsm/OZsAE6+eQa3XtKPm4f1Y9VPOzhzzIusfm0cCU1jAYgwY+FzN/PDxp2cdusLnNG7PdH1Irnt8Q/48O8jad2iEVt35jDwpudYMWtsmULL/CWpnNzjuCq/t+2Ze5k7fzUfPDGyqO3Unsfz2//3UZXnEqlN9u7dy9y5c1mxYgVxcXEMHjyYYcOGMXPmTCZNmlRmfFZWFhMnTuSVV17h1FNPLdHXo0ePoq/NDDNjw4YN9O/f/5i/j1Bq1qxZqEOQalD+wpMKMiIiIlKnvPXYVfTs3LJK59SPjuKqc30/xOXsPUjKum38+qK+AHTvmECfrol8uyKdi8/oBsDNF/v6urVvQb9urfl2ZTpRkRH8tHU3v7prVtG8Zsb69EwGnFhys8b07dmc0L5FlWLM2XuQYX94jd9fcyp9u7Uuak9sHkdG5l7y8guoFxVZpTlFaou1a9cSGRlJUlJSUVvv3r354osvAo4fP348Y8aMITExMWD/2LFjeemll9i/fz99+/Zl6NChxyTu2kT7j3ib8heeVJARERGROi8qMoLCQld0fOBgydscYhvUw8wAcAR2uL805xzm//OkLq34cvqvK42nQUw9Dhwqe6tFefYdyOOiu1/jl4M68/uRp5XoO3Aon3pRESrGiKfl5uYSHx9foi0+Pp6cnJwyY5OTk1mwYAFTpkwhPT094HxPPfUU//jHP/jmm2+YP38+MTExxyTu2iQ9PZ0uXbqEOgwJkvIXnrSHjIiIiNR5ic3iyCsoZP2mTABmf7y83LGNY2Po0zWRlz9MAWBN6k6Wrs8ocYvRix/4+tZt2kXKum2c3KMtp/U6nnWbMktssPv9qs04V7bE06tzS35IO7INHA8czOfiu1/jlJ7H8fCt55TpX526g5O6tDqiuURqq7i4OLKzs0u0ZWdn06hRoxJthYWFjB07lilTphAVVfHvliMjIxk8eDDp6elMnz79qMcsIlJdWiEjIiIidUrxPWQAZtw3jAEntmHK7y7gF7+dSfvEeM7p36HCOWZNuIzRj/+b/zfnW6IiI5j54PCi/WMAYupFcvqtL7Azax/P/PEiWjbz9b03+WrumfYJv3tyHofyCuh0XFPe/+s1lF5cc9HgJP788lcUFjoiInydA296jvTt2ezOOUDbYX/nglO6MGP8MJ5/fwnz/5fKrux9zFu0AYArh3TnT6POBGDetxu4/OwTq/uxiYRUUlIS+fn5rFu3jq5duwKwdOnSEvvBgK9Ik5yczIgRIwAoKCgAoG3btrz55pucccYZZebOz89nw4YNx/gdhJ5uefE25S88WaDf2oiIiIiEyoAT27gj2Yg3VOzUieT89z7iGkZXa57Rk97nglO6MLwaxZRDeQUMuvk5Pp16Ay2a6Jt5qQVOmRD0qVdffTVmxowZM0hJSWHo0KEsXLiwRFHGOUdGRkbR8aZNmxg0aBDp6ekkJCSwZ88ePvvsMy666CIaNGjAp59+ymWXXcbs2bO55JJLqvPOREQCMrPFzrkBwZyrW5ZERESkVnFh8u3Jo6OHsP/gke8jE8jGbXt47LZzVYyR2qFebOVjKvDUU0+xf/9+WrZsyTXXXMP06dPp0aMHaWlpxMXFkZaWhpmRmJhY9EpISACgVatWREdHY2ZMnz6dtm3b0rRpU+6++26efPLJsCjGhMMqoLpM+QtPWiEjIiIitUqvXr3c8uXl7/Eitdv69eu1MaVHKXfepvx5m/LnXVohIyIiIiK1QnlPo5LaT7nzNuXP25S/8KQVMiIiIlKrDBgwwCUnJ4c6DBEREZFKaYWMiIiI1Bl5eXmhDkGqYcuWLaEOQYKk3Hmb8udtyl94UkFGREREapXCwsJQhyDVsG/fvlCHIEFS7rxN+fM25S88qSAjIiIiIiIiIlLDVJARERGRWiU6OjrUIUg1tG3bNtQhSJCUO29T/rxN+QtPKsiIiIhIrVJQUBDqEKQatOzeu5Q7b1P+vE35C08qyIiIiEitooKMt2VmZoY6BAmScudtyp+3KX/hSQUZEREREREREZEapoKMiIiI1CpRUVGhDkGqoXnz5qEOQYKk3Hmb8udtyl94UkFGREREahUzC3UIUg0xMTGhDkGCpNx5m/LnbcpfeFJBRkRERGqVvLy8UIcg1bBly5ZQhyBBUu68TfnzNuUvPKkgIyIiIiIiIiJSw1SQERERkVolIkLfnnhZbGxsqEOQICl33qb8eZvyF570HY+IiIjUKvXq1Qt1CFINiYmJoQ5BgqTceZvy523KX3hSQUZERERqlYMHD4Y6BKmGDRs2hDoECZJy523Kn7cpf+FJBRkRERERERERkRqmgoyIiIiIHDXaA8i7lDtvU/68TfkLT+acC3UMIiIiIkUGDBjgkpOTQx2GiIiISKXMbLFzbkAw56oMJyIiIrXKoUOHQh2CVEN6enqoQ5AgKXfepvx5m/IXnlSQERERkVpFq3e97cCBA6EOQYKk3Hmb8udtyl94UkFGRERERERERKSGqSAjIiIitUrMvh2hDkGq4fjjjw91CBIk5c7blD9vU/7CkwoyIiIiUrsUFoQ6AqmG3NzcUIcgQVLuvE358zblLzypICMiIiIiR83u3btDHYIESbnzNuXP25S/8KSCjIiIiIiIiIhIDVNBRkRERESOmoSEhFCHIEFS7rxN+fM25S88qSAjIiIiIkdNVFRUqEOQICl33qb8eZvyF55UkAmSmY02syeP0lzfmVmPozGXiIiISCht3bo11CFIkJQ7b1P+vE35C0+VFmTM7EQz+8zMssxsvZkNr2T8/5nZNv/4F8wsplhfMzP7l5ntNbONZjaykrluN7NkMztoZi8F6L/KzFabWY6ZrTKzSyuYq8Jrm9m5ZrbGzPaZ2edm1r6CuaKB+4G/+o+TzOxdM9thZplmNs/MuhUbf7WZ/eD/TLab2ctm1rjYlE8AD1f0WVTEzKLN7IxFqMkAACAASURBVC0zSzUzZ2ZnVzL+qH0WIiIiIiIiIlJ1FRZkzCwKeBf4N9AMuBV41cySyhl/PnAvcC7QAegETCw25J/AIaAVcC0wvZKVIVuAR4EXAlzrOOBV4C6gMXAPMNvMWpYzV7nXNrMWwNvAA/73mQy8XkFclwBrnHOb/cdNgPeAbv75v8P3uR22ADjdOReP7zOJ8r+vw94DzjGz1hVcszJfA9cB245g7NH8LERERESKxMXFhToECZJy523Kn7cpf+HJnHPld5r1BL4FGjn/QDP7GFjknHsgwPjZQKpzbrz/+FxglnMu0cxigd1AT+fcWn//TGCzc+7eCoM0exRo65wbVaztZOB951zLYm07gGHOuW9KnV/htc3sVmCUc+60YuN3An2dc2sCxPMC8KNz7tHSff7+ZsAuoIVzblepvjjgKX/f0GLtnwCvOuderuizqIyZpQPXOefml9N/VD8LERGRo21AxwSX/NOOUIchQSooKCAyMjLUYUgQlDtvU/68TfnzLjNb7JwbEMy5ld2yZOW09SxnfA9gabHjpUArM2sOJAEFh4sAxfqD3TslGVhtZsPMLNJ/u9JBYFmAsZVdu0Tczrm9wIYKYusF/FBBbGcC24oXY8xssJllATnA5UDp/WdWA70rmPNoOdqfhYiIiEiRn376KdQhSJCUO29T/rxN+QtPlRVk1gDbgXvMrJ6Z/RI4C2hYzvg4IKvY8eGvGwXoO9zfqEoR+znnCoBXgNn4CjGzgdH+AkJlcZW+dlVja4KvsFKGmbXFd0vQXaXi/dp/y1JbfHvPpJY6Ncc/77F2tD8LEREREREREamiCgsyzrk84FLgQnx7k/weeANIL+eUXHz7uRx2+OucAH2H+3MAzOw/Zpbrf11bWeBmdh4wGTgbiMZXKJphZn2OIK4S1z6C/tJ2E6BAYWYJwMfAU8651wKd6N935iNgTqmuRsCecq5X/Brtin1OuZWND+BofxYiIiIiRfToVu9S7rxN+fM25S88VfqUJefcMufcWc655s658/FtSvtdOcNXUvK2m95Ahv/WnbVAlJl1LdW/0n+dXznn4vyvWUcQex/gS+dcsnOu0Dn3PbAIOC/A2AqvXTpu/74pnYv1l7YM360/RcysKb5izHvOuT9XEnuUf/7iTqTk7V4BOefSin1Owez8dLQ/CxEREZEiHTp0CHUIEiTlztuUP29T/sLTkTz2+iQzq29mDc3sbqA18FI5w18Bbjaz7v4Cxf2Hx/pvJXobeNjMYs3sdHxPK5pZwbWjzKw+EAlE+uM4XDr8Hjjj8IoYM+sLnEGAPWSO4Nr/Anqa2eX+6z0ILKtgE9sP8a3IORxnY2AesCDQBsVmdq1/ZYv5HyH9Z+C/xfpjgP7AJ8XaKn18dalrxPhjB4j2f1Zl9gA6Bp+FiIiISJFNmzaFOgQJknLnbcqftyl/4anSggxwPbAV314y5wK/cM4dhBK3z7QDcM59hO82os+Bjf7XQ8XmGgs08M/1GjDGOVfRyov7gf34HqV9nf/r+/3X+gKYALxlZjnAXOAx59zH/tjGm9l/juTazrkd+Dba/TO+25FOBq6uIK73gRPMrI3/eDgwEPh18duJDn8uQHdgIb7bgRbg2xD4lmLzDQPmO+e2+GNv6x+7vIIYSvsB3+dzHL7i0H6gfQ18FiIiIkeVsyP59kRqq4MHD4Y6BAmScudtyp+3KX/hqcLHXkv5/I+H7u6c+91RmGsRcLNzboX/+Dqgh3PuvurOLSIi4jW9evVyy5dX5XcSUpusX7+eLl26hDoMCYJy523Kn7cpf95VncdeqyAjIiIitUr//v3d4sWLQx2GBOnQoUNER0eHOgwJgnLnbcqftyl/3lWdgozWBIuIiEitUlBQEOoQpBqysrJCHYIESbnzNuXP25S/8KRna4mIiEitEpmbAXPvDHUYEqSEUAcgQavx3MU0gosqezipHKmsrCwSEvQv0KuUv/CkFTIiIiJSq5grDHUIIlITDuaEOgIRkZBSQUZERERERMTjWrZsGeoQpBqUv/CkgoyIiIiIiIjHmVmoQ5BqUP7CkwoyIiIiIiIiHpeRkRHqEKQalL/wpIKMiIiIiIiIiEgN01OWREREpE7YlbOf66d+woaMLGKiIumSGM8zo4eQEN8AgA8W/8QDcxaRV1BIs7gYXhp3Hh1bxZeZZ3vWPn79z0/ZtDOXQ/kFDOl1PFNvOpOoyIgK+8rzw+bd9L1nDmPP78UTNw4+4j6Arbv3MmzSv8krKKSg0HFCm6Y8e9s5NI2rD8D7yT9xzytfk1/o6N8pgRfHnUfDmHoB45jyQQp5+YXcfUk/3v3uRx5+6zsO5hXgHNw05ER+P6xf0djnPlnB4+8uwTnHr/q2Z+pNZxERYXy4JJV3v/+RZ0YPqTwhIlKjGjduHOoQpBqUv/CkFTIiIiJSJxjGHy7pxw9Tr2fZ30fSOTGee2ctBGB37gFunPYpc/7vfJb/fSS3nNeDMc/NDzjPY28nc+JxzVj295Es//tIFm/YztuLNlTaF0hBQSGjn/mcSwd1qlLfYS0a1eeLhy8j5YlrWP73kbRtHscjb30PQO7+Q9zy9Ge8f9/FrJ92A40aRPPEe/8LOM++g3lM/XAp4y44CYDEJg15/96LWPH/rmXhn69g+scr+GrVZgB+yshi4pvf8c2fr2TdP25g3dYsXv1yDQBD+3UgecN2NmzLKjdmkZqSmZnJ8OHDiY2NpX379syePbvSc4YMGYKZkZ+fX9R29tlnU79+feLi4oiLi6Nbt27HMuxjplmzZqEOQapB+QtPKsiIiIhIndCsUX3O7tm26PiUpEQ27sgGYP22LFrFNySpTVMAhvbtwLyUNHZm7y8zj2HkHDhEYaHjYF4Bh/ILOK5ZbKV9gUx6ZzEX9e9AUusmVeo7rF5UZNGKl4KCQnIPHCLCv/Hjf/63kQGdWtLVf/5tv+jJ6wvWBZxn7rcbOLP7cTSI8S2OPjkpkTbN4gCIj43hxOOasnGn7xHEb327gUsHdSIhvgEREcYt5/Xg9YU/z3vVqV154bNV5cYsUlPGjRtHdHQ0GRkZzJo1izFjxrBy5cpyx8+aNatEIaa4adOmkZubS25uLj/88MOxCvmYSk1NDXUIUg3KX3hSQUZERETqnMJCx/R5yxk2wLf6JKl1E7bt2cf3632bJs76yvcDV5q/CFHcA1cMZO2WPbS+5XkSb3mB8/u04/QT2lTaV9qy1J3MS0nj/y7qU6W+QPrc/RoJN89g3dYsHrxyUFHs7RMaFY1pl9CITbvKvh+A+Ss3c3KXVgH71mzO5Nt12xjiL2b55v156Xy7FnFs2plbdHxqt0T+u3zTEcUtcqzs3buXuXPn8sgjjxAXF8fgwYMZNmwYM2fODDg+KyuLiRMnMnny5BqOVESkfCrIiIiIeIyZnW1mzsxa1ND1RplZbuUja487nv+CuPr1uP1Xvlt04mNjeP2u8/m/l75iwB9eZ3vWfprExlAvwN4vb36znpPat2Drczez+Zlf8+WqLbz1zfpK+4rLyy/glqc/4+lbzyGy1DUq6itPyhPXkDHjZk5s25SnP15e1Y+D9F25tGrSsEz71t17ueTxD/jnb84uWjFTmcQmDUnf5am/DlIHrV27lsjISJKSkoraevfuXe4KmfHjxzNmzBgSExMD9t933320aNGC008/nfnz5x+LkI+5evUC7x8l3qD8hSdt6isiInIMmNkE4ArnXM9QxxJu7n75a9Zt3cP7911MRIQVtZ93UjvOO6kdABl79vHX95bQKcCmvv/4z1JeGHsuERFGfGwMlwzsxOcr0rni1C7l9p3T8zjOnfgOAN3aNOGv1w9mQ0YWQx97D4A9ew/igOz9h7j/8oHl9j17W/mb5daLiuTGs0/klqc/4w+X9qddi0Z8vmJzUX/ajhyOb94o4LkNoiM5kFdQom171j7Om/gO9wzrx1WndS1qb9eiUdGtXgBpO3M5vsXPxZoDhwpoEK1vISW0cnNziY8v+e83Pj6enJyyq8SSk5NZsGABU6ZMIT09vUz/448/Tvfu3YmOjmbOnDlcfPHFpKSk0Llz52MW/7HQvn37UIcg1aD8hSf931RERETqjD/N/obFP27ng/EXE1MvskTftt17SWwaS2GhY/zsb7jtlz2JrV/2N5IdWzbmo5Q0BnVN5FBeAZ8u38RlJ3eusK95owakPHFNiXl2vnhL0dcTXl9E7oG8oicpVdRX3KadOTSNjSGuQTSFhY65366nV7vmAFzQtz23P/8F67buoWvrJjz9yQquOq1LwM+lV7sW/LB5d9Hxrpz9/OLhd7j9Vyfxm/N6lBh7+SmdOfOBuTx05ck0b1Sf5z5dycgzfl6FsHpzJr071MjiLJFyxcXFkZ2dXaItOzubRo1KFiULCwsZO3YsU6ZMISoq8I8+J598ctHXN954I6+99hoffvghd9xxx9EP/BjauHGjfqj3MOUvPOmWJRERkXKY2QVm9pWZ7TazTDObZ2YnFutvY2azzGyXme0zsxQzO8fMRgEPAT38txY5fxv+r68odZ1UM7u72PFdZrbMzPaa2WYzm2Fm5e/8Wn788Wa238wuLtX+SzPLM7OW/uNJZvaDf2yqmU02s/oVzDvBzFaUaitzW5OZXWxmi83sgJn9ZGZ/NrPoqr6PI7Vy0y4eezuZLbv3ctqf3qLP3a8xfPIHRf33z/mWE3/7Kl3veIXoqAgmXXtaUd/QP79Hsn9/mSd/fSZfrd5Cr7tm0+ee10hq3YRb/EWLivqOlqfnLefBOd8C8MOW3Qx+YC4n3TWbk34/m6279zH1pjMBaNQgmmdHD+Giv7xPl9tfIWvvIe4u9ujq4i47uTPzlqYVHU/612LWbt3DM5+soM/dr9Hn7td40b9Rb6dW8TxwxSBOGf8GXe94hU6tGnPdGT8/dWZeShqXnxK48CNSU5KSksjPz2fdup83nF66dCk9epT895idnU1ycjIjRowgMTGRgQMHAtC2bVu++uqrgHObGc65Yxf8MZKXlxfqEKQalL/wZF78j42IiEhNMLPL/V8uAxoA9wP9gO5APSAF2A7cB2wGegO7gW+BR4CLgLP9c2Q55/abmQOudM69Vew6qcA059wT/uPf+a/5I9Ae+Aew1Dl3vb//bOBzIME5t7OS9/AmUOCcu7pY28tAC+fchf7jB4DP/O+hO/A08LJz7gF//yh/fHH+4wmUuh0rwJjzgTeB3wJfAu38877vnCsqPgUyoHMrlzx5REVDJAjnP/Iuk649jb6dEoKeY1fOfoZM+BffTxpBdKkVSCJBuXxq0KdeffXVmBkzZswgJSWFoUOHsnDhwhJFGeccGRkZRcebNm1i0KBBpKenk5CQwL59+1i0aBFnnXUWUVFRvP7669x6660sWbLEc4+/Xr9+PV26qFjqVcqfd5nZYufcgGDO1S1LIiIi5XDOzS1+bGa/BrKBQcCJQCJwarGiyIZiY3OBfOfctiCu+2Sxw1Qz+wPwrpnd6JwrrOJ0rwKvmVkj51yOmTUAhgOji13vkVLXewy4G3igqrEX8yfgr865F/3HG8zsj8CrZnaPK/UbITO7FbgVoG+nltW4rJRn2m/OYt3WPfQl+ILMjxnZTL/lHBVj5OiICbzn0ZF66qmnuOmmm2jZsiXNmzdn+vTp9OjRg7S0NLp3786qVato165diY18Dxw4AECrVq2IiooiKyuL+++/nzVr1hAZGckJJ5zAO++847liDECHDh1CHYJUg/IXnlSQERERKYeZdca30uVkIAHfrb4R+FZ79AWWVbZCJcjrDsG36uZEIB6IBKLxFYC2VHG6D4F9+IowrwDDAAPeLXa9K4DfAV2AOP/1qvsTd39gkL8Ic1gEvpVGicDW4oOdc88CzwL06dPHVee35hJYV/+rOgYewZjt27fTsqWKal7ktdw1a9aMd955p0x7u3btyM0N/CSwDh06lLgdKSEhge+///6YxViTMjMzPZU/KUn5C0/aQ0ZERKR87+MrxIzGV5TpC+TjK45YBedVxAU4t2hnWTNrD3wArAauxFfYuMnfXeX9V5xzefhuHbrW33Qt8LZzbp//eqcAc4B5wMX43uP9xWMKoLCi9+AXAUwE+hR7nYSvJrCjopgLCgoq6pZarvRGq+Idyp23KX/epvyFJ62QERERCcDMmuNboTLOOfe5v60fP/+/cwlwnZm1KGeVzCECrzLZAbQudp1WxY+BAfgKL//nnCvwj7momm/nVeALM+sOXABcWKzvdGBz8duW/EWhiuwAWpmZFbv1qE+pMUuAE5xz66sXuoiIiEjdpIKMiIhIYLuBncAtZrYJOA74K74VMgCzgXuBd8zsPiAd6AXk+As4qUB7fxEnzd9+EN/muePMbCFQADwGHCh23XX4Vpf8zszeBk7BdztR0JxzC8xsoz/mnf4YDlsLHGdm1wLfAOcD15SdpYT5QDNgvJnNwbdx8RWlxjwM/Nt/3TfwfW49gUHOuT9UNHnMwV3wn3uO4J1JbdQFfH+LxXMqzF10HJz7UA1GI1XVqlWrUIcg1aD8hSfdsiQiIhKAf/PcEfhus1kB/BPfJrcH/f17gbPwPZnofWAlvlt0Dq8YmYtv/5b/4ltRcrjI8Xt8T0+aD7wFzMD3pKbD112G78lEdwGrgN/g22C3umbhewrUa4dX3viv9z6+QtOT+J7s9AvgwYomcs6tBsbg24T38DmPlRozD99KnHOA7/yve/EVpypW5X2LReSYOxR4TxapPfT0XG9T/sKTHnstIiIitcqArokueer1oQ5DREr71V9DHYFUQI9N9jblz7uq89hrrZAREREREREREalhKsiIiIh4mJn9x8xyy3mND3V8IiJSM+Lj40MdglSD8heetKmviIiIt/0GaFBOX2ZNBiIiIqGjH+i9TfkLTyrIiIiIeJhzbnOoYxARkdBLS0vTHiQepvyFJ92yJCIiInXCruz9DH1wLt1ueZ6Txr7EZY++y46sfWXGTZy1EBv6BCtSdwScp6CgkHH//JTONz1Hl5tnMOOjZUfUV9ojs7+hx20v0nvcy/S/cybzFv9U1Lctcy+XPPwvThr7EieOfoFXP1tV7jx/eX1R0TyD736NlRt3HlFfaVPeWcwTc78HYPPOHM6593Xir5jKgDtnlhg39d0l9Ln95aJX48uncteznwPw4fc/MvofH5d7DZHyZGZmMnz4cGJjY2nfvj2zZ8+u9JwhQ4ZgZuTn5xe1TZs2jQEDBhATE8OoUaOOYcQiIseeVsiIiIhInWAGf7hiIGef1A6Ae56fz70vfsnzv7ugaMyS9Rl8u2YL7RIalTvPrPmrWb91D+tm/IZdOfvpe/srnNe3PR1axVfYV9qgbon8/rIBNKxfj6U/buesP77O1ldvo0FMPe567nMGdEnk3QeHsyNrH/3vnMlZvdpyfELjEnOkbNjO0x+msOqZXxNbP5qp7y7hnue/4MOHL6+wr7R9B/KY+t4SVkwfBUBcg2gmXnsa2fsOMWHWwhJj77ykH3de0g+AvPwCjrv+aUaefSIAQwd24oGZC9iwdQ+dWzc5gqyI+IwbN47o6GgyMjJISUnhwgsvpHfv3vTo0SPg+FmzZpUoxBzWpk0b7r//fubNm8f+/fuPddieEhMTE+oQpBqUv/CkFTIiIiJSJzRr1KCoGANwSrc2bNyeXXR8MC+fcU99ylPjzsPMyp3n9S/XcMsFvYiIMBLiG3LpqV1486sfKu0r7fz+HWlYvx4AJ3VMwDnHrpwDACz9aQcXDOgAQEJ8Q/p0askbAeYxg7yCQvYd9P1gmrX3IG1bNKq0r7S5C9ZyZs+2NIjxxRMfG8OZvY4nrkG9cj8HgPcXbSCxaSwDkhKL2q46I4kXPl5e4Xkixe3du5e5c+fyyCOPEBcXx+DBgxk2bBgzZ84MOD4rK4uJEycyefLkMn2XXXYZl156Kc2bNz/WYXvO8ccfH+oQpBqUv/CkgoyIiIjUOYWFjukfpjDs5J/vx39w5gKuO6c7HRMrXtmRtj2H9i1/XqnSLqExm3bmVNpXkVf+u5LOrZsUFUz6d2nFnC/W4Jzjp217WLh6S4ni0WG9O7XkruED6DDqWY67/mnmfLmGv4w6o9K+0uYv38TJ3VpXGmdpL3y8gpt+2atE26kntOG/KWlVnkvC19q1a4mMjCQpKamorXfv3qxcuTLg+PHjxzNmzBgSExMD9ktgqampoQ5BqkH5C08qyIiIiEidc8f0/xJXP5rbL+4LwDert/D92m2MvahPjcfyxfJNPDBzAa/98aKitr/dcjYZe/bR5/ZXuPPpzxhy0vHUiyz7bdnGjCze/XY9G56/hc0zb2PUeT248W//qbSvtPSdubRqGluluLdm5vLZsjSuO+fEEu2JTWNJP4IilMhhubm5ZZ4gEx8fT05O2b9HycnJLFiwgDvuuKOmwqszAt3iJd6h/IUnFWRERESkTrl7xnzWbdnN6/ddRESE79akL5ZvYk16Jh1//RwdRj1L+s4czn9gLh8vSS1zfruWjUqsVknbkc3x/pUt5fXtyt5ftAnuiL+8X9T/zeotXPfXD3nngUvp1rZZUXtCfENevedClv7zRt6fcBm5B/I48fiyt2C8+fVaenVoQWIzXzHlhnN78PmytEr7SmsQHcWBQ1X7Zv/lT1cydEBHWsQ3LNF+IK+ABjHahlCOXFxcHNnZJVeAZWdn06hRyVvsCgsLGTt2LFOmTCEqSn/HRKTuU0FGRERE6ow/vfwVi9dn8M4DlxJT7+cf6O696mS2vDqG1JduJfWlW2nbohHzHrmcX/brUGaOKwd347mPllNY6NiRtY93vlnP5acnVdjXvHEDUqbdSMq0G3n9vosB+H7tVkZMep+3xg+jX5dWJa6xK3s/+QWFAHyWksby1J1FG+cW17FVPF+v3MzeA4cA31OOerZvUWlfab06tOCH9MyqfJS89OnKMrcrAaxO20XvjglVmkvCW1JSEvn5+axbt66obenSpWU29M3OziY5OZkRI0aQmJjIwIEDAWjbti1fffVVjcbsRR07dgx1CFINyl94UulZRERE6oSVG3fy2OuLSDquKafd7XukbsdW8fzrgUsrPXfog3N5+LrTGZCUyPVDurPoh610/c0MAB4ceSqd/E8UqqivtLH//JT9B/MZPe3nx0TP/P1QenVM4LsftnLnM58RGRFBi8YNeP+h4UUbAD/9QQpbMnN5+PrBXHZ6Vxb9sJX+d75KTL1ImsTG8OJdvqdGVdRX2mWnd2XsPz9lwnWnA77Hd7cf9SwH8wrI2neQttc/zW/O71XUv2DlZnL2H+L8AAWreUtSiwpUIkciNjaWyy67jAcffJAZM2aQkpLCu+++y8KFJZ/wFR8fz5YtW4qON23axKBBg1i8eDEJCb4iYH5+Pvn5+RQUFFBQUMCBAweIiorSihpgx44d2nfHw5S/8GTOuVDHICIiIlJkQNdElzz1+lCHUeecf/9bTPr1GfTt3KryweXYlb2fIfe9wfdPXkd0vcijGJ14wq/+GvSpmZmZ3HTTTXzyySc0b96cSZMmMXLkSNLS0ujevTurVq2iXbt2Jc5JTU2lY8eO5OXlFRVcJkyYwMSJE0uMe+ihh5gwYULQsdUV69evp0uXLpUPlFpJ+fMuM1vsnBsQ1LkqyIiIiEht0j+pjVs85dpQh1HnrNu8m3VbdjN0YKeg5/h+7Vby8gs5rftxRzEy8YToODj3oVBHIRXQD/Tepvx5lwoyIiIiUmf069fPLVmyJNRhSJD27t1LbGzVnugktYNy523Kn7cpf95VnYKMNvUVERGRWkW/LPI2PbrVu5Q7b1P+vE35C08qyIiIiEitEpG7LdQhSDXs2LEj1CFIkJQ7b1P+vE35C08qyIiIiEitYmiFjIiIiNR9KsiIiIiIyFHTtGnTUIcgQVLuvE358zblLzypICMiIiIiR01cXFyoQ5AgKXfepvx5m/IXnlSQEREREZGjZtOmTaEOQYKk3Hmb8udtyl94UkFGRERERERERKSGqSBzjJjZAjPrexTmOcnMFh6NmERERESOtfr164c6BAmScudtyp+3KX/hqdKCjJndbmbJZnbQzF4K0H+Vma02sxwzW2Vml1YwVzMz+5eZ7TWzjWY2slT/uWa2xsz2mdnnZta+grlOMbNPzCzTzHaY2Ztm1jrAuGj/nOmVvM9yr20+j5vZLv9rsplZBXNdDOQ45/7nP+5pZvPMbKeZlXl0hJl1MLMPzWy3mW0zs2lmFgXgnFsG7PHPGRQzO8f/nrLMLPUIxh+1z0JERETCS9u2bUMdggRJufM25c/blL/wdCQrZLYAjwIvlO4ws+OAV4G7gMbAPcBsM2tZzlz/BA4BrYBrgelm1sM/VwvgbeABoBmQDLxeQVxNgWeBDkB7IAd4McC4e4DtFb3BI7j2rcClQG/gJOAiYHQFU94GzCx2nAe8Adxczvin/DG2BvoAZwFji/XPquR6ldmLL3/3VDbwGHwWIiIiEkZ+/PHHUIcgQVLuvE358zblLzxVWpBxzr3tnHsH2BWguy2wxzn3H+fzAb4f/juXHmhmscDlwAPOuVzn3NfAe8D1/iGXASudc2865w4AE4DeZnZCOXH9xz822zm3D5gGnF7qmh2B64C/VPI2K7v2jcDfnHPpzrnNwN+AUYEmMrNoYAjwRbFYf3DOPQ+sLOf6HYE3nHMHnHPbgI+AHsX65wPnmllMJe8jIOfcd865mcCR/Cs/ap+FiIiIhJ/CwsJQhyBBUu68TfnzNuUvPFV3D5lkYLWZDTOzSP/tSgeBZQHGJgEFzrm1xdqW8nPhoYf/GAD3/9u77/ioqvz/46+TEBJIQhISegk1NGkSRRQEWVRgbRSVJmLHgu6uYtm1ws/9umtZEZe2qIDdlaVZAVFQVBAkoaj0DlJCSYGElPP7YyZDyqQwgUwueT8fj/tY5p5zz/nc+aw+gYnrigAAIABJREFUxg/nnmttGrCV/IWJ4lxO4YLHROCvwMkSri1p7nztBeIuqCWQY60t9hGpAiYAQ4wx1d2rjvrhKsrkxrMX1yqbVmcwpq/O5nchIiIiIiIiIl6UqSBjrc0GZgHv4SrEvAfc4/6P+ILCgOMFzh0HwkvZXiRjTAfgafI8kmOMGQBUsdbOKflOzji240BYEXunROJ6fOpMLMVV1EgG9uAqdM0t0CfFPfa5dja/CxEREalkmjcvtFBaHEK5czblz9mUv8qpTAUZY0wf4J9AL6Aqrr1PphtjOnnpnoprn5m8anC6eFFkuzGmsTEmNfcoEEML4HPgIWvtt+5zoe64xpTyVs40thpAqrW20Aa9wFFKUUTKZYwJAL7EtW9LKBCDa3+cfxToGg4cK8V4f83zXU0pbRx5nM3vQkRERCqZ33//3d8hiI+UO2dT/pxN+aucyvrIUidgmbV2lbU2x1r7E7AC6OOl7yagijGmZZ5zHTn9mNEG92fAU1Rpjms/k13W2rDcI0+fWGAxMN69R0qulrg2+/3WGPM7rmJHPfcbjJp4ia3Iub21F4i7oM2uIUyDItoLqgk0Al631mZYa5NwbU7cP0889XEVvDaWNJi19u95vqvRpYwhr7P5XYiIiEglk5bmbaG0OIFy52zKn7Mpf5VTaV57XcUYEwIEAoHGmJDcVzIDPwE9clfEGGM6Az3wsoeM+zGm/wHjjDGhxpjLgOs5/TaiOcAFxphB7vmeBtZaa38rIq4GwBLg39bagitB1uMqcnRyH3cCB9x/3u1luJLmngX8xRjTwF0ceRiY4S0ua20mriJRzzyxGve4Vd2fQ3I36LXWHga2A/e6v+tIXBvn5t2npRewxFqb4b5+VGleX51n/gD3/EHucELcmw97c9a+CxERERERERHxrjQrZJ7EtSnu47jeWHTSfQ5r7VJcb+H52BiTAswG/m6tXQiex2c+zzPWfUA1XK94fh+411q7wT3WIVxvYXoe12M/XYEhxcR1J9AMeKbg40zW2ixr7e+5B3AE10a7v7v3vcEYs8EYM7yUc08FFgDrcBV7PnWfK8pUTr89Clyv5T7J6ZUkJ8m/2mUg0Bc4BGwBsoA/52kfDuQtOjUClhczf0GXu+f8DGjs/vPC3MZz/F2IiIhIJVK/fn1/hyA+Uu6cTflzNuWvcjLa+uPcMMZ8B4yx1q4p4zjtgWnW2m55zi3EtWfOr2UMU0REpMKJj6tvV23a5+8wxEdHjx4lKirK32GID5Q7Z1P+nE35cy5jzGprbbwv15Z1DxkpgrW2e1mLMe5x1uUtxrjPXaVijIiInK8ygqP9HYKUQVJSkr9DEB8pd86m/Dmb8lc5qSAjIiIiIiIiIlLOVJARERGRCiUwMNDfIUgZ1KxZ098hiI+UO2dT/pxN+aucVJARERGRCkUFGWerXr26v0MQHyl3zqb8OZvyVzmpICMiIiIVyqlTp/wdgpTBnj17/B2C+Ei5czblz9mUv8pJBRkRERERERERkXKmgoyIiIhUKAEB+nniZFp271zKnbMpf86m/FVO+sUjIiIiFUpQUJC/Q5AyqF+/vr9DEB8pd86m/Dmb8lc5qSAjIiIiFUpGRoa/Q5Ay2Lp1q79DEB8pd86m/Dmb8lc5qSAjIiIiImeNtdbfIYiPlDtnU/6cTfmrnFSQEREREZGzxhjj7xDER8qdsyl/zqb8VU5GlTgRERGpSOLj4+2qVav8HYaIiIhIiYwxq6218b5cqxUyIiIiUqFkZmb6OwQpg3379vk7BPGRcudsyp+zKX+VkwoyIiIiUqHk5OT4OwQpgxMnTvg7BPGRcudsyp+zKX+VkwoyIiIiIiIiIiLlTAUZERERqVCqVq3q7xCkDBo2bOjvEMRHyp2zKX/OpvxVTirIiIiISIWSnZ3t7xCkDLTs3rmUO2dT/pxN+aucVJARERGRCkUFGWc7cuSIv0MQHyl3zqb8OZvyVzmpICMiIiIiIiIiUs5UkBEREZEKpUqVKv4OQcogOjra3yGIj5Q7Z1P+nE35q5xUkBEREZEKxRjj7xCkDIKDg/0dgvhIuXM25c/ZlL/KSQUZERERqVAyMzP9HYKUwb59+/wdgvhIuXM25c/ZlL/KSWuCRUREpEKpevIwzH7Q32GIj1oAJPo7CvGFcudsyp+zKX+Vk1bIiIiISIVibI6/QxARERE551SQEREREREREREpZyrIiIiIiIiIiIiUMxVkRERERERERETKmQoyIiIiIiIiIiLlTAUZEREREREREZFyptdei4iIyHnjhn98wvaDyQQYQ1hIEBPv6EmnprUA2LTvKLe+vpiklHSiw0OYNeZKWtaLLDTGwoRd/PW9H1i36zBj+nXkpVu752v/6PvNjP/4J6y1GGNY/PQN1ImsXmict5b8wr8+SSAgwJCdY7nrD+148I8dAfj9aBr3TP2a7QeTyczO4W+D4hlxeWuv9/R//1vFO8s2UiXQEF6tKlPvuYJ2jaIBWLBqO2NnfUdWjqVLs1q8dX8fqgcHeR1nwqcJZGbl8Mj1FzJv5TbGfbySjMxsrIXbe7fh4esu9PT9z6L1/GPez1hr6dc5ltdu70lAgOGzn3cw76dtTL2ndymyAcNf/ZKvN+xl/9E0Ut6+h7BqVb32O5GRyW3/XszqbYeoEmB4aWR3rolvWmKbiIiIk6kgIyIiIueNmQ9cSURoMADzVm7j9klf8fOLQwAYPe0b7u/bnhGXt+adZb9xz9SvWfLsgEJjNKtTg/+M7s3sFVtIP5Wdr23VlgM8+9EKljwzgLpRoRxPyyA4KNBrLIMuacGoK9pgjCHl5Cku+PN79GrXgA5NYvjLzO+Ib16beY9fw6HjJ+ny2Af0bNuARjHh+cZI2H6IKQvX88urwwkNCeK1TxMZO2s5n/3tOlJPnuKuKUv4dvwgWtaL5M7JX/HS/DU8fePFhWI5kZHJa58lsv6V4QDUjazOgsevoX7NMI6nZdDlsQ+5uEUderRtwPYDx3nuvytZ8+JQosND6Pf8fN5Z9hsje7Wh/4VNeOqDH9n6+3Ga140oMR93/KEt/7qtB3XueKPYfi/NX0N4tapseX0km/cfo8dTs9ky8RbCqlUttk1ERMTJ9MiSiIiInDdyizEAx09kEGAMAAePn+DnbQcZelkcAEMvi+PnbQc5dPxkoTFa1Iukc7NaVAko/DPpX58k8Mi1F1I3KtQzX0hV73+/VaN6VYx7/hMZWWRm5+D+SOKOw/TtHAtArYhqdGpSi4++31xoDGMgMzuHExlZnntqGB0GwOdrdhLfrLZnlc/oKy/gw+WFxwCY/eNWLm/bgGrBrli7xtWlfs0wzz20aRDFzsMpAHz841ZuuLgZtSKqERBguKtPOz7ME9tN3Vry5pJfvM5TUO/2jagdUXj1UEEfLt/M6CsvAKBlvUjim9Xm8zU7S2wTERFxMq2QERERkfPKnZO/YmHiLqyFL568DoDdh1NpUDOMwEBXkSUwMID6NUPZnZRCrYhqpR77lz1HaFqnBpc/NZvU9EwGdm3O3wbFewovBc3/aRtPvPsDWw8c5/+GdaN9bAwAXZrV4oPlm4hvXpsdB5P5fuN+mtQKL3R9xya1+Ms1nWhy3wwiQ4OJrB7MsvEDAdh1OIXYPNc0rhXO7qQUr3F8s2EvXVvU8dr2294j/Lj5d6bec0WecWucHjcmjN2HUz2fu7Wqy6NvL+d5uhX3VZ2RQnPWCmd3UmqJbSIiIk6mFTIiIiJyXpl+7x/YNeU2/j6sG2NnLT+rY2flWNbuTGLR0zewdNxAPl+zk7eX/lZk/+suasaGV4ez6bVbeHvZRjbuPQrAy7f24MCxk3R65H0efHMZvS9oSFCVwj/Ldh5KZt5P29j6+kj2TrudUVe04daJi8847j1JqV73udl/NI3r//Ep/76zl2fFTEnqRlZnjwoiIiIiZaaCjIiIiJyXbunZmq837CEp5SSNYsLYeySV7OwcALKzc9h3JI1G0YVXpRQnNiacwZc0JzgokPBqVbn+oqas3HKApBRXcaXTI+9z8yufF7quca1wLm5Rh09Wbwdcjym989BVJL48jAVPXEtqeiZtGtQsdN1/v99C+9gYzyNSI933BNA4Jpydh06viNl1KKXI+6lWNZD0zPz74Rw8foI+z81l7HUXctOlLU/HGhPOzkPJp8c9nEqjmNPFmvRT2VQr4jEtXxWa81AKjdyPZhXXJiIi4mQqyIiIiMh5IfXkKXYfPl2gWLBqOzXDQqgZFkLtiOp0alKL95dvAuD95Zvo3LTWGT2uBDCsRxwLE3djrSUzK5uv1u2hY2wM0eHVSHhpKAkvDeXDv/QDXI8C5TqcfJKvN+yhfWPXI0tJKSfJcheHlqzbzbpdSQzrEVdovqZ1avDdr/tIS88E4LOfd3CB+w1LfTvH8tPWA2zefwyAKYvWc9OlLbzG3b5xjGd1Tu78V46bywP9OnBnn3b5+g66pDlzV27j0PGT5ORY/rN4Q76Cza97j9CxSYzn8x+encPKzb+X8hv07sZuLZi6aD0Am/cf46etBzx77BTXJiIi4mTaQ0ZERETOC2kZWdz48uekZWQRGGCoGRbCgsev8ezvMuWeXtw6cTHj/vsTUaHBzBpzpefa/s/PZ9zNXYlvUYfvft3HkH99QfLJU1gLHyzfxBv3/YGrO8Uy5LI4Vm09SNs/vUuAMVzdqTF3/KGd13imLtzAwsRdBFUJwFrLA307cFWnxgCs3HyAB99cRmCAIaZGNRY8fo3nddVTvlzHvqNpjBtyCQO7NmfF5gN0efQDgoMCiQwN5q37+wAQXq0q0+7pzTX/t4DsHEvnJrWYcNuFXmMZ2LU5903/hmdv7grAC3NWs2n/MaYuWu8pdjzUvyO39W5LszoRPDX4Yi7560cAXNWxMSN6tPKM9WXCLgZd4ir8ZGfnkLjzsGej4ULz/vNTVm45AECrh97hgkbRfPnU9QB0euR9PvvrtdSvGcbY6y9k1OuLafHALAIDDNPu6U24+y1KxbWJiIg4mbHW+jsGEREREY/45nXsqn/e7O8wzjtXj5/HC8MvpXOzWj6PkZRykt7PzuGnF26malAgP287yKQv1zH93j+cxUhFREScwwyeuNpaG+/LtXpkSURERCoUa/Tz5Fx4/c6e7D+WVqYxth1IZvJdV1A1KBCAC5vVVjFGRETER1ohIyIiIhVK+/bt7bp16/wdhvhoy5YttGjhfS8bqdiUO2dT/pxN+XMuY4xWyIiIiMj5ISBAP0+cLCxMb0ByKuXO2ZQ/Z1P+Kidt6isiIiIVStCJQzD3z/4OQ3xU198BiM+UO2dT/ooRHA79xvk7imLVquX7/l7iXPorKBEREalYbLa/IxARkfNJRoq/IyjR9u3b/R2C+IEKMiIiIiIiIiIi5UwFGRERERERERE/qlJFu4lURirIiIiIiIiIiPhRkyZN/B2C+IEKMiIiIiIiIiJ+tHv3bn+HIH6ggoyIiIiIiIiIH2VkZPg7BPEDFWRERETkvJCUfJL+4+fS6v6ZdPjTOwx8YQGHjp8o1O+5D3/EDHiV9TsPex0nOzuH+6cuofnot2hx71tMX7S+VG0FvfXVBjr86R06/fkd2j/0Nq99suaMx8nIzKLvuDnEjJxCzMgphdp3HDzuuee2Y2bxxuKi45mwYA0vzV0NwN6kVK546mMihk8i/pH3Ct3/n974hgsefJtW98/k4beWYa0F4LPV27ln8uIi5xAROR8dOXKEAQMGEBoaSmxsLO+9957XfqNHjyYsLMxzBAcHEx4e7mkfMWIE9erVo0aNGsTFxTF9+vTyugWpoLRzkIiIiJwXjDE8OqALvS5oBMDYGd/y+NvLeeOBKz19ft56kB837qdxrfCihuHdZb+x5fdjbJ40iqSUk3R++D36dGxEk9oRxbYVNKhbC0b1bosxhpSTp7jgobfpdUFDOjSpVepxAgMCeOT6LsTUCKHPM//L12atZcALn/DMzV25oWsLrLUcOn7S6z2dyMjktU8TWD/hFgDCQoJ4bsglJJ84xbMf/piv7xtfbeDXPUdY88owjDFc+/w8PvxuE0N6tKJ/l6Y89d4PbN1/jOb1IovJhojI+eP++++natWqHDhwgISEBP74xz/SsWNH2rVrl6/flClTmDLldPF81KhRBAScXgPxxBNP8MYbbxAcHMxvv/1Gr1696Ny5M126dKFx48bldj9ScWiFjIiIiJwXaoaHeIoxAJe0qsvOQ8mezxmZWdw/bQmT7umNKWacD5dv4q4r2xMQYKgVUZ0bLm7Of5dvLrGtoBrVgzHGNdOJjEwys3I8n0s7TpXAAPp0bExkaHChtsWJuwivVpUburYAXAWp2pHVvcYy+4ctXN6uAdWCXX8XFxEazOXtGhJWLahQ38Tth+jToTFBVQKpEhjAlR1jeXfZb572my5ryZtfbSjy+xMROZ+kpaUxe/Zsxo8fT1hYGN27d+e6667j7bffLtV1t956q+dcu3btCA52/fvcGIMxhq1btwJw/Pjxc3cTUmGpICMiIiLnnZwcy+Qv1nLdRc08555+/wdG9GxD0zqFV7PktetQCrF5VtA0rhXO7qTUEtu8mb9yK+0enEXs3W8y9oYutI+N8Wkcb37ZfYTo8BBu/OendP7Lu9z4z0/ZfTjFa99v1u+ha8u6pRq3S/M6zP9pG2npmaSlZzJ35VZ2Hjo9brdW9fhqnTafFJHKYdOmTQQGBhIXF+c517FjRzZsKL4wPXv2bGrVqsXll1+e7/x9991H9erVad26NfXq1aN///6ACjKVlQoyIiIiUi6MMZ8YY2aUx1xj/vM1YSFBPNC/EwA//LaPnzYf4L5+Hcpjeo/rLm7OhtdGsunft/L20l/ZuPfIWRs7KyeHJet2M35YN9a8Mpxurepx64Qvvfbdk5RCnSJWzxQ0qndberZryGVPfEi/8XO5qEUdggJP/2SsGxnKnjMsHomIOFVqaioREfkL+REREaSkeC+A55o5cyYjR470rIzMNWnSJFJSUvj2228ZOHCgZ8WMVE4qyIiIiMh55ZEZy9i8/xgfPtKfgADXD+GlG/by296jNL3nTZrc/QZ7klK5etwcFibsLHR941rh+VaE7DqUQqPosGLbkpJP0unPrg18b37pUy9j1uDiFnX5ZNX2EucordhaNejSrDatG9YEYETP1qzccsBr32pVq5CemVWqcQMCDP9v+KUk/GsEy56/kTqR1WnjngMgPTOLalW1DaGIVA5hYWEkJyfnO5ecnJxvs96Cdu/ezdKlSxk5cqTX9sDAQLp3786ePXuYPHkyALVr1z57QYtjqCAjIiIipWaMqervGIrzt3eWs3rrQeY+cS3BQaeLBo8Puoh9b97Fjml3sGPaHTSMDuPLpwdwVafYQmPceGlL/rNoHTk5lkPHTzB35VYGdWtZbFt0jWok/GsECf8awYeP/BGA3/acXg1zOPkkX6/f43lkqbg5SqvfhU3YnZTK/iNpAHyxZgcdm8R47ds+NoaNe4+Watz0U1kcT3O9fnXXoWQmfZ7IX6670NP+654jRc4jInK+iYuLIysri82bT+/zlZiYWGhD37xmzZrFpZdeSrNmzYrsA5CVleXZQ6bgShqpHPTXGyIiIpWYMeYb4DcgA8j9q7zpwGPW2hxjzA5gBtAYGAgsAm40xlwK/B9wEXAUmO++Jtk9bnVgEjAYSAMmnOt72bArib/P/om4+lFc+viHADStE8Gcx68t8dr+4+cybmg34lvU4ZaebVix6Xda3jcDgKdv6kqzuq7l6sW1FTR14ToWJuwkKDAACzzQv6OnAFTcOFO+WMu+I2mMG9YNgIvGvs+epBSOpmXQ8M7p9O0cy/T7ryQ0JIiJd/ai3/g5WCA6LIQZY67yGsvAS1pw39QlPDvENWZ2dg6x97xJRmY2x0+4xr2zTzueHdKN4ycy6PXkx57VRf8Y2Z0Lm5/+m9sv1+xkULcWJX6nIiLng9DQUAYOHMjTTz/N9OnTSUhIYN68eXz//fdFXjNr1iwee+yxfOcOHjzIkiVLuOaaa6hWrRqLFy/m/fff97xC+8CBA8WuupHzk7HW+jsGERER8RN3QaYLrqLLv4EOwH+A56y1r7gLMjWB/wf8DzBACPAD8AyuQkxN4FVgr7V2sHvcScD1wO3AXnffq4H/WWtHFRdTfIs6dtVLw87iXQrA1c/9jxdu6U7nZr4vi09KPknvZ2bz0z+HUjUo8CxGJyJyjt3wL58vPXLkCLfffjuLFi0iOjqaF154gWHDhrFr1y7atm3LL7/84nlt9Q8//ECfPn34/fff8xVYDh06xODBg0lMTCQnJ4fY2FgefPBB7rrrLgC2bNlCixYqdjuRMWa1tTbep2tVkBEREam83AWZ+kAr6/5RYIx5EhhtrW3oLsiss9Zem+eaWUCmtfaOPOc6AWuAOsAJIAm43Vr7rrs9DNgDzPVWkDHG3A3cDdC5WZ0uP7889OzfbCW3ed9RNu8/Rv8uTX0e46fNv5OZncOlreufxchERM6x4HDoN87fURTr4MGD2kfGocpSkNEjSyIiIvKjzf83ND8A440xNdyfVxXo3wVoYYy5Oc+53Iffm+MqyFR1jwOAtTbVGLOuqACstdOAaQBdunSxZfmbTPGupfsoi4tK0ScrK4sqVfQT04mUO2dT/pytZs2aJXeS84429RUREZGSpBX4HIBrn5lOeY6OuP57P4HTxRmfnDp1qiyXi5/t2LHD3yGIj5Q7Z1P+nE35q5xUQhUREZGuxhiTZ5XMJcA+a21yEW99+BloZ63d4q3RGLMFyHSPs819LhS4ANh6toMXERERcSKtkBEREZH6wKvGmFbGmMHAWKC4Z4b+AVxsjJlijOlsjGlhjLnGGDMVXI8nAW8A/zDGXGmMaQe8CZRqF9gqWQUX5IiTBAUF+TsE8ZFy52zKn7Mpf5WTVsiIiIjIu7iKJSsAi6uYUmRBxlq71hhzOa43Ly11X7sNmJOn2yNAqPvcCWCi+3OJAjNVkHGy2NhYf4cgPlLunE35czblr3LSW5ZEREQqMfdbltZbax/wdyy54lvUsau2HPB3GOKjnTt36j8sHEq5czblz9mUP+cqy1uW9MiSiIiIiJw1mZmZ/g5BfKTcOZvy52zKX+WkgoyIiIiIiIiISDnTI0siIiJSoeiRJWfLysqiShVtU+hEyp2zKX/Opvw5lx5ZEhERERGfXXbZZaxZs6bM46xdu5Zu3bqdhYjEH44cOeLvEKQMlD9nU/4qJxVkRERE5Lzx+uuvEx8fT3BwMKNGjSrU/tFHH9GmTRvCw8Np27Ytc+fOLXKsESNGUK9ePWrUqEFcXBzTp0/3tO3YsQNjDGFhYZ5j/PjxRY71448/cuWVV1KzZk1q1arFjTfeyP79+wv1O3XqFK1bt6Zhw4bF3udXX31F69atqV69OldccQU7d+70tFlreeyxx4iOjiY6OppHH32U4lZEL1iwgPDwcDp37gzA+vXrufrqq4mJicEYU6j/r7/+Su/evYmIiKBFixbMmXP65VodOnQgNDSUBQsWFBt/Xjt27OCKK66gevXqtG7dmsWLFxfZt6R7O5OxpLDk5GR/hyBloPw5m/JXOakgIyIiIueN+vXr8+STT3L77bcXatu7dy8jRozglVdeITk5mRdffJFhw4Zx8OBBr2M98cQT7Nixg+TkZObPn8+TTz7J6tWr8/U5duwYqamppKam8tRTTxUZ19GjR7n77rvZsWMHO3fuJDw8nNtuu61QvxdffJHatWsXe4+HDx9m4MCBjB8/niNHjhAfH8/NN9/saZ82bRpz584lMTGRtWvX8sknnzB16tQix5syZQq33HKL53NQUBA33XQTb7zxRqG+WVlZXH/99VxzzTUcOXKEadOmMWLECDZt2uTpc+211xY7X0FDhw6lc+fOJCUl8fzzzzN48GAOHTrktW9J93YmY4mIiPidtVaHDh06dOjQoaPCHF2a17Zl9be//c3eeuut+c79+OOPtlatWvnOxcTE2O+//77E8X777Tdbt25d++GHH1prrd2+fbsFbGZmpk/xrV692oaFheU7t23bNtu6dWv72Wef2QYNGhR57dSpU223bt08n1NTU21ISIj99ddfrbXWduvWzU6dOtXTPn36dNu1a1evY2VkZNiQkBC7e/fuQm2bN2+2rp+Kp61bt86GhobanJwcz7krr7zSPvnkk57Pv/32mw0JCbHp6elF3kOujRs32qpVq9rk5GTPue7du9vJkyd77V/cvZ3pWFJY3u9OnEf5czblz7mAVdbH3zxaISMiIiKVQnx8PG3atGH+/PlkZ2czd+5cgoOD6dChQ5HX3HfffZ7HX+rVq0f//v3ztcfGxtKwYUNuu+02Dh8+XOpYli1bRrt27fKdGzNmDH//+9+pVq1asddu2LCBjh07ej6HhobSvHlzNmzY4LW9Y8eOnraCNm/eTEBAQImPSOVy/e4sfG79+vWez/Xq1SMoKIiNGzeWON6GDRto1qwZ4eHhpYq3uHs707GkMG/5FedQ/pxN+aucVJARERGRSiEwMJCRI0cybNgwgoODGTZsGFOnTiU0NLTIayZNmkRKSgrffvstAwcOJDg4GICYmBh++ukndu7cyerVq0lJSWH48OGlimPt2rWMGzeOF1980XNuzpw5ZGVlMWDAgBKvT01NJSIiIt+5iIgIUlJSvLZHRESQmprq9cf+sWPH8hUwStK6dWtq167Niy++SGZmJgsXLmTp0qWcOHHC0+fgwYOEh4dz7NixMt9LSf3z3tuZjiWFFfX4njiD8udsyl/lpIKMiIiIVAqLFy/m0Ucf5ZtvvuHUqVMsXbqUO++8k4SEhGKvCwwMpHv37uzZs4fJkycDEBYWRnx8PFWqVKFOnTq8/vrrLFy4kOTkZHbt2pVvs9+8tmzZQr9+/ZgwYQI9evQAIC0tjUcffZSJEyeW6j7CwsKGNqENAAAYv0lEQVQKbf6YnJzsKawUbE9OTiYsLMzrBr1RUVFnVLAICgpi7ty5fPrpp9StW5eXX36Zm266qdAKm5SUFCIjI8t8LyX1z3tvZzqWiIiIv6kgIyIiIpVCQkICl19+OfHx8QQEBHDRRRfRtWvXUr+JJysri61bt3ptyy12WGtp3LixZ6Pf1NRUT5+dO3fSp08fnnrqqXyb6G7evJkdO3bQo0cP6taty8CBA9m/fz9169Zlx44dheZq164diYmJns9paWls3brV8whUwfbExMRCj0flatmyJdZa9u7dW6rvAFxvUlq6dClJSUl8+eWXbNu2jYsvvtjTfvLkSU6dOkWrVq1KHKtdu3Zs27YtX1GouHiLu7czHUsKK7jCSJxF+XM25a9yUkFGREREzhtZWVmkp6eTnZ1NdnY26enpZGVlAXDRRRfx7bffelbErFmzhm+//dbrHjIHDx7kgw8+IDU1lezsbL788kvef/99evfuDcCKFSvYuHEjOTk5JCUl8eCDD9KrV68if1Dv3buX3r17c//99zN69Oh8bRdccAG7d+8mISGBhIQEpk+fTp06dUhISKBRo0aFxhowYADr169n9uzZpKenM27cODp06EDr1q0BGDlyJK+88gp79+5l3759vPzyy15fAQ6uFS99+vRh6dKlnnPWWtLT0zl16hQA6enpZGRkeNrXrl1Leno6J06c4KWXXmL//v35xl+zZg29e/f2PN41Y8YMmjRp4nX+uLg4OnXqxHPPPUd6ejpz5sxh7dq1DBo0yGv/4u7tTMeSwvQfhM6m/Dmb8ldJ+bobsA4dOnTo0KFDx7k4OrZqan31zDPPWCDf8cwzz3jaJ06caJs3b27DwsJs06ZN7UsvveRpe/75523fvn2ttdYePHjQXn755TYiIsKGh4fbCy64wE6bNs3T97333rNNmjSx1atXt3Xr1rW33HKL3b9/f5FxPfvssxawoaGh+Q5vvv7660JvWWrbtq195513PJ8XLVpkW7VqZUNCQmzPnj3t9u3bPW05OTl27NixNioqykZFRdmxY8fmeytSQZ988onnvq09/QapvEdsbKyn/ZFHHrGRkZE2NDTU9u3b127evDnfeD179rTz5s3zfB43bpwdNmxYkfNv377d9uzZ04aEhNi4uDi7aNEiT9uyZcvyfU8l3VtxY0nJCuZSnEX5czblz7kow1uWjOt6ERERkYqhffv2dt26df4Oo1Lp3r07EydOpHPnzmUaZ926dYwcOZI1a9Z4zl111VVMmDCBNm3alDVMOce2bNlCixYt/B2G+Ej5czblz7mMMauttfG+XFvlbAcjIiIiUhbeNp+Vc+u77747K+O0b9+e+fPn5zu3cOHCszK2nHu5j5mJMyl/zqb8VU7aQ0ZEREQqlKpVq/o7BCkDb/veiDMod86m/Dmb8lc5qSAjIiIiFUp26hF/hyBl4O3NUOIMyp2zKX/OpvxVTirIiIiISIUSmJnm7xCkDHLfaiXOo9w5m/LnbMpf5aSCjIiIiIiIiIhIOVNBRkRERETOmqZNm/o7BPGRcudsyp+zKX+VkwoyIiIiInLWHDp0yN8hiI+UO2dT/pxN+aucVJARERERkbMmNTXV3yGIj5Q7Z1P+nE35q5xUkBERERGp5C677DLWrFlT5nHWrl3LTTfddBYiEhEROf+pICMiIiLnjddff534+HiCg4MZNWpUvrZTp04xePBgmjRpgjGGb775ptixduzYQf/+/YmKiqJu3bo88MADnrdg/PLLL8THxxMVFUVUVBR9+vThl19+KXKskubOyMhg9OjR1KlTh5o1a3Lttdeyd+9er2OVNPezzz5LUFAQYWFhnmPbtm1FxrZgwQLCw8Pp3LkzADNnzqRLly7UqFGDhg0b8uijj+Z7+8eRI0cYMGAAoaGhxMbG8t5773naOnToQK1atViwYEHRX2wBO3bs4IorrqB69eq0bt2axYsXF9nXWstjjz1GdHQ00dHRPProo1hrfRpLCqtXr56/Q5AyUP6cTfmrnFSQERERkfNG/fr1efLJJ7n99tu9tnfv3p133nmHunXrljjWfffdR+3atdm/fz8JCQksXbqUSZMmeeb5+OOPOXLkCIcPH+a6665jyJAhxY5X3NwTJkzghx9+YO3atezbt4/IyEjGjBlT5D2WNPfNN99Mamqq52jWrFmRcU2ZMoVbbrnF8/nEiRO8+uqrHD58mBUrVvDVV1/x0ksvedrvv/9+qlatyoEDB3j33Xe599572bBhg6d98ODBTJ06tdjvIq+hQ4fSuXNnkpKSeP755xk8eHCReylMmzaNuXPnkpiYyNq1a/nkk0/yzXUmY0lheu2usyl/zqb8VU4qyIiIiMh5Y+DAgdxwww1ER0cXaqtatSp/+tOf6N69O4GBgSWOtX37dm666SZCQkKoW7cuffv29RQeIiMjPatdrLUEBgayZcuWIscqae7t27dz9dVXU6dOHUJCQhgyZEi+IkdeZzp3cU6dOsWSJUvo2bOn59y9995Ljx49qFq1Kg0aNGD48OEsX74cgLS0NGbPns348eMJCwuje/fuXHfddbz99tue69u0acNXX31FRkZGifNv2rSJn3/+meeee45q1aoxaNAg2rdvz+zZs732nzlzJg8//DANGzakQYMGPPzww8yYMcOnsaQwFa+cTflzNuWvclJBRkRERMSLhx56iA8++IATJ06wd+9ePv/8c/r27ZuvT2RkJCEhIYwZM4a//vWvPs91xx13sHz5cvbt28eJEyd499136devX7HXFDf3ggULqFmzJu3atWPy5MlFjrF582YCAgJo2LBhkX2WLVtGu3btAFfRIzAwkLi4OE97x44d8xWP6tatS1BQEBs3biw2foANGzbQrFkzwsPDixyvYP+OHTt67XumY4mIiPhbFX8HICIiIlIR9ezZk//85z/UqFGD7Oxsbr31Vm644YZ8fY4dO0ZaWhozZ84kNjbW57ni4uJo3LgxDRo0IDAwkPbt2/P6668Xe01Rc990003cfffd1KlThxUrVjBo0CAiIyMZOnSo1zHyFjAKeuutt1i1ahXTp08HXG8BiYiIyNcnIiKClJQUz+eoqCjCw8M5duxYifdd1HhF7Z9TsH9ERASpqalYa894LCksKirK3yFIGSh/zqb8VU5aISMiIiJSQE5ODldffTUDBw4kLS2Nw4cPc/ToUR577LFCfUNDQxk9ejQjR47k4MGD7Nq1K9+GuqVx7733kp6eTlJSEmlpaQwcOLDEFTLe5gZo27Yt9evXJzAwkEsvvZSHHnqIjz/+2Ov1UVFR+Yopec2dO5fHH3+czz//nJiYGADCwsJITk7O1y85OTlfUScsLIyUlBQiIyNLjL804xXXPzk5mbCwMIwxZzyWFFba/79KxaT8OZvyVzmpICMiIiJSwJEjR9i9ezcPPPAAwcHBREdHc9ttt/HZZ5957Z+Tk+N5tKlx48b5NtQtjcTEREaNGkXNmjUJDg5mzJgxrFy5ksOHD5d4bd65vcnda8abli1bYq0tdO0XX3zBXXfdxYIFC2jfvr3nfFxcHFlZWWzevDlf7LmPNAGsWrWKU6dO0apVqxJjb9euHdu2bctXFCo4XsH+iYmJXvue6VhS2O7du/0dgpSB8udsyl/lpIKMiIiInDeysrJIT08nOzub7Oxs0tPT8725IiMjg/T0dMC1oW16errXYkVMTAxNmzZl8uTJZGVlcezYMWbOnOnZv2TRokWsWbOG7OxskpOT+ctf/kJUVBRt2rQpMrbi5r7ooouYNWsWx48fJzMzk0mTJlG/fn3PypS8Spp73rx5HD16FGstK1eu5LXXXuP666/3GlNQUBB9+vRh6dKlnnNLlixh+PDhzJ49m4svvjhf/9DQUAYOHMjTTz9NWloay5cvZ968efne0rRixQp69+5NcHAwADNmzKBJkyZe54+Li6NTp04899xzpKenM2fOHNauXcugQYO89h85ciSvvPIKe/fuZd++fbz88sue15uf6VgiIiJ+Z63VoUOHDh06dOioMEen1k2tr5555hkL5DueeeYZT3tsbGyh9u3bt1trrX3++edt3759PX3XrFlje/bsaSMjI210dLQdPHiwPXDggLXW2o8++si2atXKhoaG2piYGNuvXz+bmJhYbGzFzX348GE7bNgwW6tWLRsREWEvu+wyu2LFCs+1bdu2te+8806p5h4yZIitWbOmDQ0Nta1atbITJkwoNq5PPvkk33336tXLBgYG2tDQUM+Rtz0pKclef/31tnr16rZRo0b23XffzTde79697bx58zyfx40bZ4cNG1bk/Nu3b7c9e/a0ISEhNi4uzi5atMjTtmzZMhsaGur5nJOTY8eOHWujoqJsVFSUHTt2rM3JySnVWFKy3bt3+zsEKQPlz9mUP+cCVlkff/MY1/UiIiIiFUN8fLxdtWqVv8OoVLp3787EiRPp3LlzmcZZt24dd999Nz/88IPn3FVXXcWECROKXT0kIiLiVMaY1dbaeJ+uVUFGREREKpL27dvbdevW+TsM8dG2bdto1qyZv8MQHyh3zqb8OZvy51xlKchoDxkREREROWtycnL8HYL4SLlzNuXP2ZS/ykkFGRERERERERGRcqaCjIiIiFQouW/nEWdq3ry5v0MQHyl3zqb8OZvyVzmpICMiIiIVSmZmpr9DkDL4/fff/R2C+Ei5czblz9mUv8pJBRkRERGpUPQcvbOlpaX5OwTxkXLnbMqfsyl/lZMKMiIiIiIiIiIi5UwFGREREalQgoKC/B2ClEH9+vX9HYL4SLlzNuXP2ZS/ykkFGREREalQrLX+DkHKICMjw98hiI+UO2dT/pxN+aucVJARERGRCiUrK8vfIUgZJCUl+TsE8ZFy52zKn7Mpf5WTCjIiIiIiIiIiIuXMaFmwiIiIVCTGmBRgo7/jEJ/FAIf9HYT4RLlzNuXP2ZQ/52plrQ335cIqZzsSERERkTLaaK2N93cQ4htjzCrlz5mUO2dT/pxN+XMuY8wqX6/VI0siIiIiIiIiIuVMBRkRERERERERkXKmgoyIiIhUNNP8HYCUifLnXMqdsyl/zqb8OZfPudOmviIiIiIiIiIi5UwrZEREREREREREypkKMiIiIuIXxpi+xpiNxpgtxpjHvbQbY8xr7va1xpgL/RGnFFaK3A1352ytMeZ7Y0xHf8Qp3pWUvzz9LjLGZBtjBpdnfFK80uTPGNPLGJNgjNlgjFla3jGKd6X4d2eEMWaBMSbRnbvb/BGnFGaMedMYc9AYs76Idp9+s6ggIyIiIuXOGBMI/BvoB7QFhhpj2hbo1g9o6T7uBiaXa5DiVSlztx3oaa3tAIxHeyNUGKXMX26/fwBflm+EUpzS5M8YEwlMAq6z1rYDbiz3QKWQUv6zdz/wi7W2I9ALeNkYU7VcA5WizAD6FtPu028WFWRERETEHy4Gtlhrt1lrTwEfANcX6HM9MMu6/AhEGmPqlXegUkiJubPWfm+tPer++CPQsJxjlKKV5p89gDHAbOBgeQYnJSpN/oYB/7PW7gKw1iqHFUNpcmeBcGOMAcKAI0BW+YYp3lhrl+HKR1F8+s2igoyIiIj4QwNgd57Pe9znzrSPlL8zzcsdwOfnNCI5EyXmzxjTABgATCnHuKR0SvPPXxwQZYz5xhiz2hgzstyik+KUJnevA22AfcA64CFrbU75hCdl5NNvlirnLBwRERGRohkv5wq++rE0faT8lTovxpgrcBVkup/TiORMlCZ/rwKPWWuzXX9RLxVIafJXBegC/AGoBvxgjPnRWrvpXAcnxSpN7q4GEoDeQHNgkTHmW2tt8rkOTsrMp98sKsiIiIiIP+wBGuX53BDX3wieaR8pf6XKizGmAzAd6GetTSqn2KRkpclfPPCBuxgTA/Q3xmRZa+eWT4hSjNL+u/OwtTYNSDPGLAM6AirI+Fdpcncb8IK11gJbjDHbgdbAyvIJUcrAp98semRJRERE/OEnoKUxpql7w8IhwPwCfeYDI91vLrgEOG6t3V/egUohJebOGNMY+B9wi/5WvsIpMX/W2qbW2ibW2ibAx8B9KsZUGKX5d+c8oIcxpooxpjrQFfi1nOOUwkqTu124VjZhjKkDtAK2lWuU4iuffrNohYyIiIiUO2ttljHmAVxvcAkE3rTWbjDGjHa3TwE+A/oDW4ATuP7mUPyslLl7GogGJrlXWWRZa+P9FbOcVsr8SQVVmvxZa381xnwBrAVygOnWWq+v6pXyU8p/9sYDM4wx63A9AvOYtfaw34IWD2PM+7jefBVjjNkDPAMEQdl+sxjXaigRERERERERESkvemRJRERERERERKScqSAjIiIiIiIiIlLOVJARERERERERESlnKsiIiIiIiIiIiJQzFWRERERERERERMqZCjIiIiIiIuc5Y8wAY4w1xrTOc66XMeaTszD2DGPM4BL69DLGXHqG4/Zyx3xHnnOd3eceKeHaG4wxbYtpH22MGXkGsVhjzNt5Plcxxhw6G99fcbEZY0YZY+r7MMYOY0zM2Y5NRM4uFWRERERERM5/Q4HvgCF+mr8XcEYFGbd1wM15Pg8BEktx3Q2A14KMMaaKtXaKtXbWGcSRBlxgjKnm/nwlsPcMri+1ArGNAs64ICMizqCCjIiIiIjIecwYEwZcBtxB4YJMDWPMHGPML8aYKcaYAGNMoHvVy3pjzDpjzJ/d43QyxvxojFnrvibKy1yelRnGmHhjzDfGmCbAaODPxpgEY0wPY0wtY8xsY8xP7uOyIsLfBYQYY+oYYwzQF/g8z3zNjTFfGGNWG2O+Nca0dq/EuQ540T1fc3ccfzfGLAUeMsY8m7vKxhjTwhiz2BiTaIz52RjTvIhYPgf+6P7zUOD9PHFcbIz53hizxv2/rdznqxtjPnJ/Zx8aY1YYY+LdbanGmOfd8/5ojKnjPv+sMeYR96qjeOBd931U8/b9uv8cbYxZ6J5/KmDyxDbCGLPSPcZUY0xgEfcnIuVMBRkRERERkfPbDcAX1tpNwBFjzIV52i4GHgbaA82BgUAnoIG19gJrbXvgLXffWcBj1toOuFauPFOaya21O4ApwL+stZ2std8CE9yfLwIGAdOLGeJj4EZcK2x+BjLytE0DxlhruwCPAJOstd8D84Gx7vm2uvtGWmt7WmtfLjD+u8C/rbUd3XPsLyKOD4AhxpgQoAOwIk/bb8Dl1trOwNPA393n7wOOur+z8UCXPNeEAj+6510G3JV3Mmvtx8AqYLj7Pk4WERe4cvGde/75QGMAY0wbXCuMLrPWdgKygeHFjCMi5aiKvwMQEREREZFzaijwqvvPH7g//+z+vNJauw3AGPM+0B34CmhmjJkIfAosNMZE4CpoLHVfNxP4bxli6gO0dS16AVwrdcKttSle+n4EfAi0xrUq5VJ3vGHuP/83zzjBxcz5YcETxphwXMWnOQDW2vSiLrbWrnWv9hkKfFagOQKYaYxpCVggyH2+O67iE9ba9caYtXmuOQXk7kGzGtdjUL66HFcxDWvtp8aYo+7zf8BVBPrJ/R1VAw6WYR4ROYtUkBEREREROU8ZY6KB3rj2P7FAIGCNMY+6u9gCl1hr7VFjTEfgauB+4Cbgz6WcMovTq/BDiukXAHQrYdVHbkC/G2MycRUsHuL0XjQBwDH3yo/SSPNyzng5V5z5wEu49sSJznN+PPC1tXaAu2jzTSnGz7TW5n7/2ZTuv82K+34L5jJ3/pnW2idKMbaIlDM9siQiIiIicv4aDMyy1sZaa5tYaxsB23Gt3AC42BjT1BgTgOvRlu/ce5QEWGtnA08BF1prjwNHjTE93NfdAiylsB2cfixnUJ7zKUB4ns8LgQdyPxhjSiqqPI3rcans3BPW2mRguzHmRvcYxl1I8jafV+4x9hhjbnCPEWyMqV7MJW8C46y16wqcj+D0Jr+j8pz/DldBC+N661P7kmIqoOB97MD797sM96NIxph+QO7+Pl8Bg40xtd1tNY0xsWcYg4icIyrIiIiIiIicv4YCcwqcmw0Mc//5B+AFYD2uQs0coAHwjTEmAZgB5K6uuBXXRrlrce0zM87LfM8BE4wx3+Ja9ZFrATAgd1Nf4EEg3r3Z7S+4Nv0tkrX2e2vtXC9Nw4E7jDGJwAbgevf5D4Cx7k1ui9qkN9ctwIPu+/oeqFtMHHustRO8NP0T+D9jzHJcq5ByTQJqucd+DFgLHC8hnrxmAFNyN/Wl6O/3OeByY8zPwFW4NkPGWvsL8CSux87WAouAemcwv4icQ+b0KjkRERERERE5W9xvNAqy1qa7C0NfAXHW2lN+Dk1EKgDtISMiIiIiInJuVAe+NsYE4drP5V4VY0Qkl1bIiIiIiIiIiIiUM+0hIyIiIiIiIiJSzlSQEREREREREREpZyrIiIiIiIiIiIiUMxVkRERERERERETKmQoyIiIiIiIiIiLlTAUZEREREREREZFy9v8B6nlYFwlDFqEAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"tpr = aqp.plot_group_metric(clean_xtab, 'tpr', min_group_size=0.05)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fpr = aqp.plot_group_metric(clean_xtab, 'fpr', min_group_size=0.05)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"tnr = aqp.plot_group_metric(clean_xtab, 'tnr', min_group_size=0.05)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"# Bias Report - http://aequitas.dssg.io/audit/eg1cp7ku/adult_rf_binary/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Train Uncertainty Estimation Model with TF Probability"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Instructions\n",
"- Using MPG model from earlier, create uncertainty estimation model with TF Probability.\n",
"- In particular, we will focus on building a model that accounts for Aleatoric Uncertainty."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow_probability as tfp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Aleatoric Uncertainty\n",
"- Known Unknowns\n",
"- 2 Main Changes to our network \n",
" - Add a second unit to the last dense layer before passing it to Tensorflow Probability layer to model for the predictor y and the heteroscedasticity or unequal scattering of data\n",
" - DistributionLambda distribution mean and std\n",
"- DistributionLambda is a special Keras layer that uses a Python lambda to construct a distribution based on the layer inputs and the output of the final layer of the model is passed into the loss function. Lastly. this model will return a distribution for both mean and standard deviation."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 314.000000\n",
"mean 23.310510\n",
"std 7.728652\n",
"min 10.000000\n",
"25% 17.000000\n",
"50% 22.000000\n",
"75% 28.950000\n",
"max 46.600000\n",
"Name: MPG, dtype: float64"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_label_stats = train_labels.describe()\n",
"train_label_stats"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"def build_seq_prob_model(feature_layer):\n",
" model = tf.keras.Sequential([\n",
" feature_layer,\n",
" tf.keras.layers.Dense(150, activation='relu'),\n",
" tf.keras.layers.Dense(75, activation='relu'),\n",
" tf.keras.layers.Dense(1 + 1),\n",
" tfp.layers.DistributionLambda( \n",
" lambda t:tfp.distributions.Normal(\n",
" loc=t[..., :1],\n",
" scale=1e-3 + tf.math.softplus(0.1 * t[...,1:])\n",
"\n",
" )\n",
" )\n",
" \n",
" \n",
" \n",
" ])\n",
" return model\n",
"\n",
"def build_prob_model(train_ds, feature_layer, epochs=5, loss_metric='mse'):\n",
" model = build_seq_prob_model(feature_layer)\n",
" negloglik = lambda y, rv_y: -rv_y.log_prob(y)\n",
" loss = negloglik\n",
" model.compile(tf.optimizers.Adam(learning_rate=0.05), loss=loss, metrics=[loss_metric])\n",
" history = model.fit(train_ds, \n",
" epochs=epochs)\n",
" return model, history"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train Probability Model"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train for 3 steps\n",
"Epoch 1/500\n",
"3/3 [==============================] - 1s 179ms/step - loss: 289.4115 - mse: 467.4192\n",
"Epoch 2/500\n",
"3/3 [==============================] - 0s 8ms/step - loss: 10.8904 - mse: 2146.0674\n",
"Epoch 3/500\n",
"3/3 [==============================] - 0s 8ms/step - loss: 14.6148 - mse: 14412.0020\n",
"Epoch 4/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 11.0708 - mse: 22909.2715\n",
"Epoch 5/500\n",
"3/3 [==============================] - 0s 8ms/step - loss: 7.9969 - mse: 25712.4395\n",
"Epoch 6/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 6.2809 - mse: 16896.6328\n",
"Epoch 7/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.5337 - mse: 13097.0459\n",
"Epoch 8/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.3558 - mse: 8430.8945\n",
"Epoch 9/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.4545 - mse: 12015.9473\n",
"Epoch 10/500\n",
"3/3 [==============================] - 0s 8ms/step - loss: 5.6325 - mse: 13679.8428\n",
"Epoch 11/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.7443 - mse: 16258.0684\n",
"Epoch 12/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.8145 - mse: 22385.1602\n",
"Epoch 13/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.7975 - mse: 18070.9668\n",
"Epoch 14/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.7592 - mse: 20072.0234\n",
"Epoch 15/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.6765 - mse: 14435.9951\n",
"Epoch 16/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.6073 - mse: 12951.8135\n",
"Epoch 17/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.5773 - mse: 17503.4219\n",
"Epoch 18/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.5553 - mse: 13661.9014\n",
"Epoch 19/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.5370 - mse: 15361.4971\n",
"Epoch 20/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.5176 - mse: 12288.7988\n",
"Epoch 21/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.5191 - mse: 15276.9297\n",
"Epoch 22/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.5170 - mse: 14089.5273\n",
"Epoch 23/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.5058 - mse: 14584.8838\n",
"Epoch 24/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.5000 - mse: 14928.4043\n",
"Epoch 25/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.4856 - mse: 12734.4443\n",
"Epoch 26/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.4612 - mse: 12625.1104\n",
"Epoch 27/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.4397 - mse: 12854.3057\n",
"Epoch 28/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.4243 - mse: 11512.9736\n",
"Epoch 29/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.4245 - mse: 11075.1084\n",
"Epoch 30/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.4086 - mse: 11876.9541\n",
"Epoch 31/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.3661 - mse: 10930.6514\n",
"Epoch 32/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.3685 - mse: 12469.8594\n",
"Epoch 33/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.3587 - mse: 8965.7744\n",
"Epoch 34/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.3283 - mse: 8561.2461\n",
"Epoch 35/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.3072 - mse: 8928.2119\n",
"Epoch 36/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.3035 - mse: 10619.9385\n",
"Epoch 37/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.2834 - mse: 9783.4971\n",
"Epoch 38/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.2622 - mse: 9843.2666\n",
"Epoch 39/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.2393 - mse: 8357.2910\n",
"Epoch 40/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.2145 - mse: 7278.5747\n",
"Epoch 41/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.2047 - mse: 6345.6763\n",
"Epoch 42/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.1892 - mse: 8294.3887\n",
"Epoch 43/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.1743 - mse: 8486.4980\n",
"Epoch 44/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.1345 - mse: 7920.7007\n",
"Epoch 45/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.1337 - mse: 7080.7983\n",
"Epoch 46/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.0975 - mse: 6087.7061\n",
"Epoch 47/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.0788 - mse: 6589.9492\n",
"Epoch 48/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 5.0501 - mse: 6192.0493\n",
"Epoch 49/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.0322 - mse: 6017.4409\n",
"Epoch 50/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.0095 - mse: 5554.2471\n",
"Epoch 51/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 5.0024 - mse: 4325.9233\n",
"Epoch 52/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.9542 - mse: 4978.0015\n",
"Epoch 53/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.9307 - mse: 5045.3882\n",
"Epoch 54/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 4.8945 - mse: 5324.7930\n",
"Epoch 55/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.8652 - mse: 4882.1841\n",
"Epoch 56/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.8393 - mse: 4989.3301\n",
"Epoch 57/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.8120 - mse: 5418.7661\n",
"Epoch 58/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.7821 - mse: 4176.5176\n",
"Epoch 59/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 4.7469 - mse: 3908.3801\n",
"Epoch 60/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.7173 - mse: 4154.9175\n",
"Epoch 61/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.6813 - mse: 4125.5449\n",
"Epoch 62/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.6446 - mse: 3797.4419\n",
"Epoch 63/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.6100 - mse: 4114.7046\n",
"Epoch 64/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.5891 - mse: 3232.7366\n",
"Epoch 65/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.5263 - mse: 3249.9463\n",
"Epoch 66/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.5095 - mse: 3122.7322\n",
"Epoch 67/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.4396 - mse: 3672.9690\n",
"Epoch 68/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.4033 - mse: 3165.2065\n",
"Epoch 69/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.3750 - mse: 2781.8916\n",
"Epoch 70/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 4.3060 - mse: 3513.2612\n",
"Epoch 71/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.3048 - mse: 3017.0720\n",
"Epoch 72/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.2555 - mse: 3009.1721\n",
"Epoch 73/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 4.2044 - mse: 2485.9214\n",
"Epoch 74/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 4.1642 - mse: 2432.0781\n",
"Epoch 75/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 4.1191 - mse: 2212.7798\n",
"Epoch 76/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.0274 - mse: 2043.6230\n",
"Epoch 77/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 4.0115 - mse: 2192.0579\n",
"Epoch 78/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.9804 - mse: 1996.8037\n",
"Epoch 79/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.9469 - mse: 1831.0070\n",
"Epoch 80/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.8745 - mse: 1631.5665\n",
"Epoch 81/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.8452 - mse: 1783.3835\n",
"Epoch 82/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.8408 - mse: 1548.9960\n",
"Epoch 83/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.7867 - mse: 1195.3561\n",
"Epoch 84/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.7306 - mse: 1533.9740\n",
"Epoch 85/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.7335 - mse: 1377.3910\n",
"Epoch 86/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.6876 - mse: 1008.8643\n",
"Epoch 87/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.6106 - mse: 962.4722\n",
"Epoch 88/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.6333 - mse: 1129.8300\n",
"Epoch 89/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.6078 - mse: 1049.6285\n",
"Epoch 90/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.5650 - mse: 903.0133\n",
"Epoch 91/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.4980 - mse: 995.9970\n",
"Epoch 92/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.5344 - mse: 996.9030\n",
"Epoch 93/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.5386 - mse: 786.8526\n",
"Epoch 94/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.5165 - mse: 887.1656\n",
"Epoch 95/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 3.4390 - mse: 948.1669\n",
"Epoch 96/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 3.5098 - mse: 630.7740\n",
"Epoch 97/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.4685 - mse: 548.3526\n",
"Epoch 98/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.3634 - mse: 638.3589\n",
"Epoch 99/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 3.2947 - mse: 505.8619\n",
"Epoch 100/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.2976 - mse: 556.7950\n",
"Epoch 101/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 3.2066 - mse: 472.4757\n",
"Epoch 102/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.1598 - mse: 512.0362\n",
"Epoch 103/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.1335 - mse: 416.4638\n",
"Epoch 104/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.1083 - mse: 479.9660\n",
"Epoch 105/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.0651 - mse: 373.9520\n",
"Epoch 106/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 3.0468 - mse: 355.4185\n",
"Epoch 107/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.0042 - mse: 315.7336\n",
"Epoch 108/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.9150 - mse: 265.0731\n",
"Epoch 109/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.9673 - mse: 258.4703\n",
"Epoch 110/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.1160 - mse: 205.9806\n",
"Epoch 111/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 3.4447 - mse: 218.9561\n",
"Epoch 112/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.9588 - mse: 166.8366\n",
"Epoch 113/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.9732 - mse: 171.0196\n",
"Epoch 114/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.8975 - mse: 127.5398\n",
"Epoch 115/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.8193 - mse: 133.0668\n",
"Epoch 116/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.7153 - mse: 116.9886\n",
"Epoch 117/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.6874 - mse: 122.8846\n",
"Epoch 118/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.6012 - mse: 93.8380\n",
"Epoch 119/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.5609 - mse: 71.0378\n",
"Epoch 120/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.4820 - mse: 58.9037\n",
"Epoch 121/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.5064 - mse: 54.4231\n",
"Epoch 122/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.3928 - mse: 41.4275\n",
"Epoch 123/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.3762 - mse: 37.1680\n",
"Epoch 124/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.3225 - mse: 35.2728\n",
"Epoch 125/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.2709 - mse: 22.8382\n",
"Epoch 126/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.3638 - mse: 22.9883\n",
"Epoch 127/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.2370 - mse: 15.8560\n",
"Epoch 128/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.2747 - mse: 18.4363\n",
"Epoch 129/500\n",
"3/3 [==============================] - 0s 8ms/step - loss: 2.3348 - mse: 15.3111\n",
"Epoch 130/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.2085 - mse: 16.3434\n",
"Epoch 131/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.2278 - mse: 18.1785\n",
"Epoch 132/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.2948 - mse: 18.4050\n",
"Epoch 133/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.2015 - mse: 15.9371\n",
"Epoch 134/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.1820 - mse: 14.6961\n",
"Epoch 135/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1677 - mse: 18.4769\n",
"Epoch 136/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1503 - mse: 14.6882\n",
"Epoch 137/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.1935 - mse: 14.3779\n",
"Epoch 138/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.3238 - mse: 20.2727\n",
"Epoch 139/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.2447 - mse: 15.6502\n",
"Epoch 140/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.2014 - mse: 18.0907\n",
"Epoch 141/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.1363 - mse: 17.3442\n",
"Epoch 142/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.1556 - mse: 19.4936\n",
"Epoch 143/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0995 - mse: 14.6296\n",
"Epoch 144/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1166 - mse: 14.8993\n",
"Epoch 145/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1476 - mse: 13.0640\n",
"Epoch 146/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0864 - mse: 14.4030\n",
"Epoch 147/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1665 - mse: 14.8962\n",
"Epoch 148/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.3017 - mse: 16.8323\n",
"Epoch 149/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.3100 - mse: 15.4963\n",
"Epoch 150/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.1513 - mse: 18.7443\n",
"Epoch 151/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1485 - mse: 18.7525\n",
"Epoch 152/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1056 - mse: 12.5500\n",
"Epoch 153/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.2017 - mse: 20.0671\n",
"Epoch 154/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.1957 - mse: 16.1199\n",
"Epoch 155/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.2127 - mse: 11.8819\n",
"Epoch 156/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.2664 - mse: 13.7478\n",
"Epoch 157/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.2296 - mse: 12.7940\n",
"Epoch 158/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0899 - mse: 14.6433\n",
"Epoch 159/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1562 - mse: 13.2895\n",
"Epoch 160/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0927 - mse: 12.5513\n",
"Epoch 161/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0379 - mse: 11.3670\n",
"Epoch 162/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0199 - mse: 10.2426\n",
"Epoch 163/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0457 - mse: 11.5835\n",
"Epoch 164/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1591 - mse: 11.7080\n",
"Epoch 165/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.4938 - mse: 14.8084\n",
"Epoch 166/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.3912 - mse: 14.7808\n",
"Epoch 167/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.4455 - mse: 17.7141\n",
"Epoch 168/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.3625 - mse: 18.1681\n",
"Epoch 169/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.2424 - mse: 24.1672\n",
"Epoch 170/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.3134 - mse: 22.5646\n",
"Epoch 171/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.2255 - mse: 20.1390\n",
"Epoch 172/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1632 - mse: 14.6248\n",
"Epoch 173/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1006 - mse: 15.7575\n",
"Epoch 174/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0895 - mse: 14.5912\n",
"Epoch 175/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0214 - mse: 14.5205\n",
"Epoch 176/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0348 - mse: 15.8724\n",
"Epoch 177/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0236 - mse: 11.3826\n",
"Epoch 178/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9954 - mse: 8.8911\n",
"Epoch 179/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0725 - mse: 9.6624\n",
"Epoch 180/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0093 - mse: 11.2822\n",
"Epoch 181/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0275 - mse: 8.9602\n",
"Epoch 182/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9516 - mse: 9.5548\n",
"Epoch 183/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9680 - mse: 10.0730\n",
"Epoch 184/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9782 - mse: 10.1105\n",
"Epoch 185/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9818 - mse: 9.9838\n",
"Epoch 186/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9121 - mse: 10.2850\n",
"Epoch 187/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9488 - mse: 9.4263\n",
"Epoch 188/500\n",
"3/3 [==============================] - ETA: 0s - loss: 1.9304 - mse: 10.55 - 0s 9ms/step - loss: 1.9890 - mse: 8.8250\n",
"Epoch 189/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0437 - mse: 8.7912\n",
"Epoch 190/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.2470 - mse: 9.9531\n",
"Epoch 191/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0561 - mse: 10.0001\n",
"Epoch 192/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0670 - mse: 12.5298\n",
"Epoch 193/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0177 - mse: 10.2433\n",
"Epoch 194/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9484 - mse: 9.4764\n",
"Epoch 195/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0344 - mse: 11.5727\n",
"Epoch 196/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0047 - mse: 10.1634\n",
"Epoch 197/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0105 - mse: 9.0040\n",
"Epoch 198/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0962 - mse: 11.2893\n",
"Epoch 199/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0761 - mse: 11.2578\n",
"Epoch 200/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.2335 - mse: 13.2755\n",
"Epoch 201/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.2632 - mse: 16.6926\n",
"Epoch 202/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1649 - mse: 13.7505\n",
"Epoch 203/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0772 - mse: 13.8323\n",
"Epoch 204/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.1182 - mse: 12.5733\n",
"Epoch 205/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0008 - mse: 11.6717\n",
"Epoch 206/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0108 - mse: 13.8384\n",
"Epoch 207/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9820 - mse: 9.1166\n",
"Epoch 208/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0297 - mse: 9.4805\n",
"Epoch 209/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0161 - mse: 8.9316\n",
"Epoch 210/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9960 - mse: 10.1038\n",
"Epoch 211/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9950 - mse: 9.5736\n",
"Epoch 212/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9838 - mse: 9.0330\n",
"Epoch 213/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9296 - mse: 10.6439\n",
"Epoch 214/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9245 - mse: 8.1212\n",
"Epoch 215/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9315 - mse: 8.4200\n",
"Epoch 216/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1962 - mse: 11.1917\n",
"Epoch 217/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0492 - mse: 12.0217\n",
"Epoch 218/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.2284 - mse: 12.0312\n",
"Epoch 219/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9685 - mse: 8.9652\n",
"Epoch 220/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0179 - mse: 12.4205\n",
"Epoch 221/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9962 - mse: 9.7718\n",
"Epoch 222/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0335 - mse: 11.6692\n",
"Epoch 223/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0037 - mse: 9.7447\n",
"Epoch 224/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0585 - mse: 11.0403\n",
"Epoch 225/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0009 - mse: 10.8745\n",
"Epoch 226/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9285 - mse: 9.2395\n",
"Epoch 227/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9458 - mse: 8.9418\n",
"Epoch 228/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8624 - mse: 5.9536\n",
"Epoch 229/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9416 - mse: 7.3899\n",
"Epoch 230/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.1287 - mse: 9.9609\n",
"Epoch 231/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9942 - mse: 8.8317\n",
"Epoch 232/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8391 - mse: 9.1421\n",
"Epoch 233/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9327 - mse: 8.7558\n",
"Epoch 234/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8608 - mse: 9.9360\n",
"Epoch 235/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8726 - mse: 7.4110\n",
"Epoch 236/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8575 - mse: 8.6415\n",
"Epoch 237/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8683 - mse: 7.6467\n",
"Epoch 238/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1848 - mse: 10.2426\n",
"Epoch 239/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1915 - mse: 9.8874\n",
"Epoch 240/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1164 - mse: 10.9549\n",
"Epoch 241/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9143 - mse: 7.2587\n",
"Epoch 242/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9635 - mse: 9.8617\n",
"Epoch 243/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8889 - mse: 8.2110\n",
"Epoch 244/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8623 - mse: 8.8742\n",
"Epoch 245/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9968 - mse: 9.9350\n",
"Epoch 246/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8680 - mse: 9.2997\n",
"Epoch 247/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8155 - mse: 8.1788\n",
"Epoch 248/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8277 - mse: 7.8912\n",
"Epoch 249/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8149 - mse: 6.9927\n",
"Epoch 250/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7787 - mse: 6.6637\n",
"Epoch 251/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7593 - mse: 6.8745\n",
"Epoch 252/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8143 - mse: 8.4326\n",
"Epoch 253/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9000 - mse: 7.0930\n",
"Epoch 254/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9418 - mse: 7.0057\n",
"Epoch 255/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.1137 - mse: 8.3917\n",
"Epoch 256/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0853 - mse: 11.0257\n",
"Epoch 257/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0626 - mse: 9.0026\n",
"Epoch 258/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9080 - mse: 8.7226\n",
"Epoch 259/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9195 - mse: 8.8690\n",
"Epoch 260/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9412 - mse: 9.0000\n",
"Epoch 261/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.8442 - mse: 7.1750\n",
"Epoch 262/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.8545 - mse: 7.9121\n",
"Epoch 263/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7789 - mse: 7.3553\n",
"Epoch 264/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9119 - mse: 9.6287\n",
"Epoch 265/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8096 - mse: 7.3038\n",
"Epoch 266/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9068 - mse: 8.6525\n",
"Epoch 267/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9987 - mse: 7.0086\n",
"Epoch 268/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0520 - mse: 7.7630\n",
"Epoch 269/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9514 - mse: 6.8309\n",
"Epoch 270/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9448 - mse: 7.2687\n",
"Epoch 271/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8886 - mse: 8.2606\n",
"Epoch 272/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8466 - mse: 7.5506\n",
"Epoch 273/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9672 - mse: 9.9356\n",
"Epoch 274/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1142 - mse: 11.7509\n",
"Epoch 275/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.2349 - mse: 10.5136\n",
"Epoch 276/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1597 - mse: 10.8940\n",
"Epoch 277/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1115 - mse: 12.6330\n",
"Epoch 278/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0745 - mse: 10.4279\n",
"Epoch 279/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1614 - mse: 15.2771\n",
"Epoch 280/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9636 - mse: 11.4373\n",
"Epoch 281/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9998 - mse: 13.0749\n",
"Epoch 282/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9615 - mse: 11.9984\n",
"Epoch 283/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9138 - mse: 10.1295\n",
"Epoch 284/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.8630 - mse: 9.9451\n",
"Epoch 285/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8156 - mse: 9.1568\n",
"Epoch 286/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8819 - mse: 9.8814\n",
"Epoch 287/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.7983 - mse: 7.2535\n",
"Epoch 288/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9600 - mse: 8.0732\n",
"Epoch 289/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.8329 - mse: 7.1464\n",
"Epoch 290/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8018 - mse: 5.8100\n",
"Epoch 291/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7306 - mse: 6.9214\n",
"Epoch 292/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.8712 - mse: 7.6397\n",
"Epoch 293/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9144 - mse: 7.8787\n",
"Epoch 294/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9059 - mse: 8.1137\n",
"Epoch 295/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9613 - mse: 7.9304\n",
"Epoch 296/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8650 - mse: 7.4138\n",
"Epoch 297/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9187 - mse: 8.4685\n",
"Epoch 298/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9426 - mse: 8.8860\n",
"Epoch 299/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8385 - mse: 8.0443\n",
"Epoch 300/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8060 - mse: 9.0011\n",
"Epoch 301/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7833 - mse: 8.7140\n",
"Epoch 302/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.7721 - mse: 7.9643\n",
"Epoch 303/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8438 - mse: 6.8327\n",
"Epoch 304/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9253 - mse: 8.2149\n",
"Epoch 305/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8101 - mse: 8.9616\n",
"Epoch 306/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9278 - mse: 7.6164\n",
"Epoch 307/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8373 - mse: 6.0805\n",
"Epoch 308/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0951 - mse: 7.8605\n",
"Epoch 309/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9120 - mse: 8.0000\n",
"Epoch 310/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9290 - mse: 10.4318\n",
"Epoch 311/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9278 - mse: 8.2355\n",
"Epoch 312/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9052 - mse: 8.5550\n",
"Epoch 313/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8611 - mse: 9.1160\n",
"Epoch 314/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8694 - mse: 10.1777\n",
"Epoch 315/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7660 - mse: 6.8480\n",
"Epoch 316/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7056 - mse: 6.6457\n",
"Epoch 317/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7809 - mse: 7.3483\n",
"Epoch 318/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.7854 - mse: 5.4756\n",
"Epoch 319/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9736 - mse: 7.2505\n",
"Epoch 320/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.8293 - mse: 7.5544\n",
"Epoch 321/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1158 - mse: 8.3757\n",
"Epoch 322/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8971 - mse: 8.9563\n",
"Epoch 323/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9711 - mse: 7.9511\n",
"Epoch 324/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8831 - mse: 9.2324\n",
"Epoch 325/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8698 - mse: 10.0753\n",
"Epoch 326/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8139 - mse: 10.6471\n",
"Epoch 327/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8312 - mse: 7.3422\n",
"Epoch 328/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7698 - mse: 6.3697\n",
"Epoch 329/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6790 - mse: 6.5160\n",
"Epoch 330/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7000 - mse: 6.1959\n",
"Epoch 331/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6874 - mse: 5.2238\n",
"Epoch 332/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8833 - mse: 6.2380\n",
"Epoch 333/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8701 - mse: 5.6685\n",
"Epoch 334/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9126 - mse: 6.9169\n",
"Epoch 335/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9042 - mse: 6.8527\n",
"Epoch 336/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8945 - mse: 8.1961\n",
"Epoch 337/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8185 - mse: 8.1658\n",
"Epoch 338/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7649 - mse: 7.7965\n",
"Epoch 339/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7685 - mse: 7.4102\n",
"Epoch 340/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.7877 - mse: 6.4303\n",
"Epoch 341/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7696 - mse: 5.2003\n",
"Epoch 342/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7567 - mse: 6.2427\n",
"Epoch 343/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8211 - mse: 7.1904\n",
"Epoch 344/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0095 - mse: 9.3450\n",
"Epoch 345/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9024 - mse: 7.4722\n",
"Epoch 346/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9420 - mse: 8.0202\n",
"Epoch 347/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9899 - mse: 7.1297\n",
"Epoch 348/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9046 - mse: 10.2319\n",
"Epoch 349/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9255 - mse: 9.2003\n",
"Epoch 350/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.7811 - mse: 7.8847\n",
"Epoch 351/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.7014 - mse: 7.3091\n",
"Epoch 352/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.6850 - mse: 7.1049\n",
"Epoch 353/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.8297 - mse: 6.1370\n",
"Epoch 354/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7397 - mse: 5.4892\n",
"Epoch 355/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7830 - mse: 5.1554\n",
"Epoch 356/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8294 - mse: 6.0769\n",
"Epoch 357/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.8255 - mse: 5.0741\n",
"Epoch 358/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7675 - mse: 6.5781\n",
"Epoch 359/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.8411 - mse: 6.3339\n",
"Epoch 360/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6941 - mse: 6.0606\n",
"Epoch 361/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6756 - mse: 6.3196\n",
"Epoch 362/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7866 - mse: 5.4643\n",
"Epoch 363/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.7055 - mse: 5.3291\n",
"Epoch 364/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8147 - mse: 5.2417\n",
"Epoch 365/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7942 - mse: 7.2440\n",
"Epoch 366/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7853 - mse: 7.7551\n",
"Epoch 367/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.1110 - mse: 9.6039\n",
"Epoch 368/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9849 - mse: 12.2760\n",
"Epoch 369/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8884 - mse: 10.8983\n",
"Epoch 370/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9848 - mse: 9.6114\n",
"Epoch 371/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9587 - mse: 11.6348\n",
"Epoch 372/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8127 - mse: 9.8999\n",
"Epoch 373/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7416 - mse: 7.8438\n",
"Epoch 374/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6914 - mse: 8.3747\n",
"Epoch 375/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6348 - mse: 5.9558\n",
"Epoch 376/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6035 - mse: 5.5476\n",
"Epoch 377/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6643 - mse: 5.3513\n",
"Epoch 378/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9051 - mse: 6.2475\n",
"Epoch 379/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7943 - mse: 5.0474\n",
"Epoch 380/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7532 - mse: 6.1194\n",
"Epoch 381/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6696 - mse: 6.1284\n",
"Epoch 382/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.7978 - mse: 7.8826\n",
"Epoch 383/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7242 - mse: 5.7984\n",
"Epoch 384/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7608 - mse: 6.6420\n",
"Epoch 385/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6450 - mse: 5.5312\n",
"Epoch 386/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8621 - mse: 6.8061\n",
"Epoch 387/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9931 - mse: 9.4243\n",
"Epoch 388/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.9190 - mse: 8.1463\n",
"Epoch 389/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.7930 - mse: 9.8425\n",
"Epoch 390/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.7873 - mse: 10.3152\n",
"Epoch 391/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8143 - mse: 10.3236\n",
"Epoch 392/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6753 - mse: 8.4923\n",
"Epoch 393/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6309 - mse: 6.6079\n",
"Epoch 394/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6227 - mse: 6.2439\n",
"Epoch 395/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6069 - mse: 5.2066\n",
"Epoch 396/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6391 - mse: 4.4511\n",
"Epoch 397/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8082 - mse: 4.6536\n",
"Epoch 398/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6710 - mse: 4.4417\n",
"Epoch 399/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6786 - mse: 5.0901\n",
"Epoch 400/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.5957 - mse: 5.2160\n",
"Epoch 401/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6012 - mse: 5.3559\n",
"Epoch 402/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.5824 - mse: 4.9971\n",
"Epoch 403/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6988 - mse: 5.8426\n",
"Epoch 404/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6574 - mse: 5.2186\n",
"Epoch 405/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6871 - mse: 6.3779\n",
"Epoch 406/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7346 - mse: 5.8178\n",
"Epoch 407/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6228 - mse: 5.7095\n",
"Epoch 408/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8443 - mse: 5.2450\n",
"Epoch 409/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7299 - mse: 5.9811\n",
"Epoch 410/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.8231 - mse: 6.2995\n",
"Epoch 411/500\n",
"3/3 [==============================] - 0s 8ms/step - loss: 1.6938 - mse: 4.9252\n",
"Epoch 412/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6507 - mse: 4.5467\n",
"Epoch 413/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6583 - mse: 6.4584\n",
"Epoch 414/500\n",
"3/3 [==============================] - ETA: 0s - loss: 1.6602 - mse: 6.506 - 0s 9ms/step - loss: 1.6518 - mse: 4.8465\n",
"Epoch 415/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.7282 - mse: 5.5770\n",
"Epoch 416/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8187 - mse: 5.9847\n",
"Epoch 417/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.8555 - mse: 7.3748\n",
"Epoch 418/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8692 - mse: 8.2919\n",
"Epoch 419/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7410 - mse: 7.7576\n",
"Epoch 420/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6743 - mse: 6.5697\n",
"Epoch 421/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7037 - mse: 7.7682\n",
"Epoch 422/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8997 - mse: 5.8701\n",
"Epoch 423/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6393 - mse: 5.4788\n",
"Epoch 424/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.8004 - mse: 6.0465\n",
"Epoch 425/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.6823 - mse: 5.9955\n",
"Epoch 426/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6983 - mse: 5.2214\n",
"Epoch 427/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6694 - mse: 6.9777\n",
"Epoch 428/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.6656 - mse: 5.1774\n",
"Epoch 429/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.7376 - mse: 5.4188\n",
"Epoch 430/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6165 - mse: 4.2728\n",
"Epoch 431/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7101 - mse: 5.4711\n",
"Epoch 432/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.5182 - mse: 4.9340\n",
"Epoch 433/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6648 - mse: 5.6271\n",
"Epoch 434/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7070 - mse: 4.2049\n",
"Epoch 435/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7264 - mse: 5.2568\n",
"Epoch 436/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0444 - mse: 6.5036\n",
"Epoch 437/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8927 - mse: 8.2541\n",
"Epoch 438/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8460 - mse: 9.7183\n",
"Epoch 439/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7935 - mse: 10.5578\n",
"Epoch 440/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7041 - mse: 9.4646\n",
"Epoch 441/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6646 - mse: 7.7201\n",
"Epoch 442/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6387 - mse: 7.7690\n",
"Epoch 443/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.5902 - mse: 5.8785\n",
"Epoch 444/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6417 - mse: 5.9745\n",
"Epoch 445/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.5803 - mse: 3.9934\n",
"Epoch 446/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7558 - mse: 4.2028\n",
"Epoch 447/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 2.0171 - mse: 4.5414\n",
"Epoch 448/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9922 - mse: 5.5157\n",
"Epoch 449/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0359 - mse: 7.4637\n",
"Epoch 450/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8297 - mse: 8.6698\n",
"Epoch 451/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9435 - mse: 9.1461\n",
"Epoch 452/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9597 - mse: 10.7977\n",
"Epoch 453/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9098 - mse: 9.1330\n",
"Epoch 454/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8410 - mse: 8.7817\n",
"Epoch 455/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8368 - mse: 7.6799\n",
"Epoch 456/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7873 - mse: 8.5852\n",
"Epoch 457/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6983 - mse: 7.2708\n",
"Epoch 458/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7030 - mse: 6.5638\n",
"Epoch 459/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.5793 - mse: 6.0257\n",
"Epoch 460/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.5426 - mse: 5.2043\n",
"Epoch 461/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.5487 - mse: 3.4369\n",
"Epoch 462/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6616 - mse: 3.7768\n",
"Epoch 463/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6673 - mse: 4.1853\n",
"Epoch 464/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6082 - mse: 4.7531\n",
"Epoch 465/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7348 - mse: 5.7014\n",
"Epoch 466/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7201 - mse: 7.2246\n",
"Epoch 467/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6697 - mse: 7.2543\n",
"Epoch 468/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6101 - mse: 6.6284\n",
"Epoch 469/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6355 - mse: 5.1163\n",
"Epoch 470/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6260 - mse: 5.3089\n",
"Epoch 471/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6425 - mse: 5.5649\n",
"Epoch 472/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8033 - mse: 5.7500\n",
"Epoch 473/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0394 - mse: 6.7638\n",
"Epoch 474/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7465 - mse: 6.1286\n",
"Epoch 475/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8929 - mse: 6.0468\n",
"Epoch 476/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.9021 - mse: 9.4820\n",
"Epoch 477/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7700 - mse: 9.1397\n",
"Epoch 478/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.7502 - mse: 7.5406\n",
"Epoch 479/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.7246 - mse: 8.0890\n",
"Epoch 480/500\n",
"3/3 [==============================] - 0s 10ms/step - loss: 1.6397 - mse: 5.0125\n",
"Epoch 481/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6681 - mse: 6.4276\n",
"Epoch 482/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.5601 - mse: 5.8989\n",
"Epoch 483/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6255 - mse: 5.3218\n",
"Epoch 484/500\n",
"3/3 [==============================] - ETA: 0s - loss: 2.0615 - mse: 6.604 - 0s 9ms/step - loss: 1.8755 - mse: 5.7796\n",
"Epoch 485/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7913 - mse: 6.6218\n",
"Epoch 486/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7614 - mse: 7.7303\n",
"Epoch 487/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6672 - mse: 6.1732\n",
"Epoch 488/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.6242 - mse: 5.5691\n",
"Epoch 489/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.5992 - mse: 7.4229\n",
"Epoch 490/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.5186 - mse: 4.9881\n",
"Epoch 491/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.4862 - mse: 3.9572\n",
"Epoch 492/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.3807 - mse: 3.3100\n",
"Epoch 493/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.4038 - mse: 3.9184\n",
"Epoch 494/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.5796 - mse: 3.5789\n",
"Epoch 495/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 2.0799 - mse: 8.0165\n",
"Epoch 496/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8525 - mse: 8.0849\n",
"Epoch 497/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.8373 - mse: 11.1524\n",
"Epoch 498/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7403 - mse: 10.4774\n",
"Epoch 499/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7141 - mse: 9.8791\n",
"Epoch 500/500\n",
"3/3 [==============================] - 0s 9ms/step - loss: 1.7138 - mse: 9.5976\n"
]
}
],
"source": [
"prob_model, history = build_prob_model(train_ds, dense_feature_layer, epochs=500)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extract Mean and Standard Deviation Output"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"feature_column_list = categorical_feature_list + numerical_feature_list\n",
"x_tst = dict(test_data[feature_column_list])\n",
"yhat = prob_model(x_tst)\n",
"prob_preds = prob_model.predict(test_ds)\n",
"m = yhat.mean()\n",
"s = yhat.stddev()"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(10, 1), dtype=float32, numpy=\n",
"array([[13.778888],\n",
" [10.670513],\n",
" [11.68947 ],\n",
" [23.891275],\n",
" [17.854294],\n",
" [12.198006],\n",
" [13.530302],\n",
" [12.123651],\n",
" [19.382854],\n",
" [29.686583]], dtype=float32)>"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m[0:10]"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(10, 1), dtype=float32, numpy=\n",
"array([[0.9538786 ],\n",
" [0.88148165],\n",
" [0.74826884],\n",
" [1.6325067 ],\n",
" [1.4162414 ],\n",
" [0.82958436],\n",
" [0.85223436],\n",
" [0.80150706],\n",
" [1.0225366 ],\n",
" [3.4836817 ]], dtype=float32)>"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[0:10]"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"prob_outputs = {\n",
" \"pred\": prob_preds.flatten(),\n",
" \"actual_value\": test_data['MPG'].values,\n",
" \"pred_mean\": m.numpy().flatten(),\n",
" \"pred_std\": s.numpy().flatten()\n",
"}\n",
"prob_output_df = pd.DataFrame(prob_outputs)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pred</th>\n",
" <th>actual_value</th>\n",
" <th>pred_mean</th>\n",
" <th>pred_std</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>25.960018</td>\n",
" <td>15.0</td>\n",
" <td>13.778888</td>\n",
" <td>0.953879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15.523413</td>\n",
" <td>10.0</td>\n",
" <td>10.670513</td>\n",
" <td>0.881482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>17.726507</td>\n",
" <td>9.0</td>\n",
" <td>11.689470</td>\n",
" <td>0.748269</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>27.666529</td>\n",
" <td>25.0</td>\n",
" <td>23.891275</td>\n",
" <td>1.632507</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>19.945644</td>\n",
" <td>19.0</td>\n",
" <td>17.854294</td>\n",
" <td>1.416241</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pred actual_value pred_mean pred_std\n",
"0 25.960018 15.0 13.778888 0.953879\n",
"1 15.523413 10.0 10.670513 0.881482\n",
"2 17.726507 9.0 11.689470 0.748269\n",
"3 27.666529 25.0 23.891275 1.632507\n",
"4 19.945644 19.0 17.854294 1.416241"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prob_output_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"a = plt.axes(aspect='equal')\n",
"plt.scatter(test_labels, m.numpy().flatten())\n",
"plt.xlabel('True Values [MPG]')\n",
"plt.ylabel('Predictions [MPG]')\n",
"lims = [0, 50]\n",
"plt.xlim(lims)\n",
"plt.ylim(lims)\n",
"_ = plt.plot(lims, lims)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Model Interpretability with Shapley Values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Using Open Source Library Shap - https://github.com/slundberg/shap\n",
"- Developed by Su-In Lee's lab at the University of Washington, and Microsoft Research"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"# adapted from https://github.com/kweinmeister/notebooks/blob/master/tensorflow-shap-college-debt.ipynb\n",
"# https://towardsdatascience.com/understand-how-your-tensorflow-model-is-making-predictions-d0b3c7e88500"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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RegExp(\"^(data|aria)-[\"+r.ATTRIBUTE_NAME_CHAR+\"]*$\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\"accept-charset\",className:\"class\",htmlFor:\"for\",httpEquiv:\"http-equiv\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\"value\");\"number\"!==t.type||!1===t.hasAttribute(\"value\")?t.setAttribute(\"value\",\"\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\"value\",\"\"+e)}}};t.exports=s},function(t,e,n){\"use strict\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\"use strict\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\"105\",h.displayName||h.name||\"Component\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\"object\"!=typeof b||Array.isArray(b))&&a(\"106\",this.getName()||\"ReactCompositeComponent\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\".componentWillUnmount()\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\"object\"!=typeof n.childContextTypes&&a(\"107\",this.getName()||\"ReactCompositeComponent\");for(var i in e)i in n.childContextTypes||a(\"108\",this.getName()||\"ReactCompositeComponent\",i);return u({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?d.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o&&a(\"136\",this.getName()||\"ReactCompositeComponent\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\"function\"==typeof c?c.call(n,o,t,e):c)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\"110\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\"use strict\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\"source\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e),M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topReset\",\"reset\",e),M.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\"65\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function v(t){var e=t.type;h(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\"__html\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},K={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\"ReactDOMComponent\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(f,this);break;case\"input\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\"option\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\"select\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\"textarea\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\"foreignobject\"===p)&&(a=x.html),a===x.html&&(\"svg\"===this._tag?a=x.svg:\"math\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\"script\"===this._tag){var g=v.createElement(\"div\"),m=this._currentElement.type;g.innerHTML=\"<\"+m+\"></\"+m+\">\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\"/>\":w+\">\"+k+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return h},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\"style\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+C.createMarkupForRoot()),n+=\" \"+C.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return G[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"option\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"select\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"textarea\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\"textarea\":O.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(p,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(\"style\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\"style\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\"style\"===r)if(c?c=this._previousStyleCopy=m({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return j(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"input\":case\"textarea\":L.stopTracking(this);break;case\"html\":case\"head\":case\"body\":g(\"66\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(97),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" 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c=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\"90\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\"checked\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\"\"===r.value)r.value=\"0\";else if(\"number\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\"\"+i)}else r.value!==\"\"+i&&(r.value=\"\"+i);else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=l.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=p},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(162),c=(n(2),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){a.getNodeFromInstance(t).setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length;return{start:o,end:o+r}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(405),l=n(172),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\" /react-text \"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\"\\x3c!--\"+o+\"--\\x3e\"+h+\"\\x3c!-- /react-text --\\x3e\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n&&r(\"67\",this._domID),8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\"91\"),a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a&&o(\"92\"),Array.isArray(c)&&(c.length<=1||o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t||c(\"33\"),\"_hostNode\"in e||c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in 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i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof Symbol&&Symbol.for&&Symbol.for(\"react.element\")||60103;t.exports=r},function(t,e,n){\"use strict\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\"use strict\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function 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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import shap\n",
"shap.initjs()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train Model without Dense Features"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"def build_shap_model( num_features):\n",
" model = tf.keras.Sequential([\n",
" tf.keras.layers.Dense(64, activation='relu', input_shape=[num_features]),\n",
" tf.keras.layers.Dense(64, activation='relu'),\n",
" tf.keras.layers.Dense(1)\n",
" ])\n",
"\n",
" optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
"\n",
" model.compile(loss='mse',\n",
" optimizer=optimizer,\n",
" metrics=['mae', 'mse'])\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"shap_num_features = len( numerical_feature_list)\n",
"shap_feature_columns = tf_numerical_feature_list\n",
"#shap_feature_layer = tf.keras.layers.DenseFeatures(shap_feature_columns)\n",
"shap_model = build_shap_model( shap_num_features)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 314 samples\n",
"Epoch 1/1000\n",
"314/314 [==============================] - 0s 1ms/sample - loss: 583.9959 - mae: 22.9326 - mse: 583.9960\n",
"Epoch 2/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 535.7158 - mae: 21.8546 - mse: 535.7158\n",
"Epoch 3/1000\n",
"314/314 [==============================] - 0s 62us/sample - loss: 487.4761 - mae: 20.7324 - mse: 487.4761\n",
"Epoch 4/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 433.8281 - mae: 19.4080 - mse: 433.8281\n",
"Epoch 5/1000\n",
"314/314 [==============================] - 0s 58us/sample - loss: 373.7382 - mae: 17.8423 - mse: 373.7382\n",
"Epoch 6/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 308.9781 - mae: 15.9864 - mse: 308.9781\n",
"Epoch 7/1000\n",
"314/314 [==============================] - 0s 56us/sample - loss: 244.3900 - mae: 14.0646 - mse: 244.3900\n",
"Epoch 8/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 183.3589 - mae: 12.0997 - mse: 183.3589\n",
"Epoch 9/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 130.1076 - mae: 10.0379 - mse: 130.1077\n",
"Epoch 10/1000\n",
"314/314 [==============================] - 0s 58us/sample - loss: 88.8453 - mae: 8.1383 - mse: 88.8454\n",
"Epoch 11/1000\n",
"314/314 [==============================] - 0s 67us/sample - loss: 61.1359 - mae: 6.4886 - mse: 61.1359\n",
"Epoch 12/1000\n",
"314/314 [==============================] - 0s 59us/sample - loss: 46.9610 - mae: 5.4498 - mse: 46.9610\n",
"Epoch 13/1000\n",
"314/314 [==============================] - 0s 56us/sample - loss: 40.7491 - mae: 4.9472 - mse: 40.7491\n",
"Epoch 14/1000\n",
"314/314 [==============================] - 0s 59us/sample - loss: 36.3882 - mae: 4.6591 - mse: 36.3882\n",
"Epoch 15/1000\n",
"314/314 [==============================] - 0s 60us/sample - loss: 32.3769 - mae: 4.3938 - mse: 32.3769\n",
"Epoch 16/1000\n",
"314/314 [==============================] - ETA: 0s - loss: 25.6739 - mae: 4.1236 - mse: 25.673 - 0s 58us/sample - loss: 28.7494 - mae: 4.1113 - mse: 28.7494\n",
"Epoch 17/1000\n",
"314/314 [==============================] - 0s 59us/sample - loss: 25.5186 - mae: 3.9192 - mse: 25.5186\n",
"Epoch 18/1000\n",
"314/314 [==============================] - 0s 56us/sample - loss: 22.4260 - mae: 3.6503 - mse: 22.4260\n",
"Epoch 19/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 20.1525 - mae: 3.4243 - mse: 20.1525\n",
"Epoch 20/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 18.0679 - mae: 3.2722 - mse: 18.0679\n",
"Epoch 21/1000\n",
"314/314 [==============================] - 0s 56us/sample - loss: 16.4449 - mae: 3.1248 - mse: 16.4450\n",
"Epoch 22/1000\n",
"314/314 [==============================] - 0s 55us/sample - loss: 15.2274 - mae: 2.9795 - mse: 15.2274\n",
"Epoch 23/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 14.0818 - mae: 2.8733 - mse: 14.0818\n",
"Epoch 24/1000\n",
"314/314 [==============================] - 0s 56us/sample - loss: 13.1804 - mae: 2.7693 - mse: 13.1804\n",
"Epoch 25/1000\n",
"314/314 [==============================] - 0s 56us/sample - loss: 12.5132 - mae: 2.6745 - mse: 12.5132\n",
"Epoch 26/1000\n",
"314/314 [==============================] - 0s 65us/sample - loss: 11.8062 - mae: 2.6014 - mse: 11.8062\n",
"Epoch 27/1000\n",
"314/314 [==============================] - 0s 54us/sample - loss: 11.3952 - mae: 2.5356 - mse: 11.3952\n",
"Epoch 28/1000\n",
"314/314 [==============================] - 0s 56us/sample - loss: 11.0135 - mae: 2.5058 - mse: 11.0135\n",
"Epoch 29/1000\n",
"314/314 [==============================] - 0s 55us/sample - loss: 10.5466 - mae: 2.4279 - mse: 10.5466\n",
"Epoch 30/1000\n",
"314/314 [==============================] - 0s 54us/sample - loss: 10.2533 - mae: 2.3836 - mse: 10.2533\n",
"Epoch 31/1000\n",
"314/314 [==============================] - 0s 55us/sample - loss: 9.9811 - mae: 2.3419 - mse: 9.9811\n",
"Epoch 32/1000\n",
"314/314 [==============================] - 0s 63us/sample - loss: 9.6811 - mae: 2.2968 - mse: 9.6811\n",
"Epoch 33/1000\n",
"314/314 [==============================] - 0s 56us/sample - loss: 9.5506 - mae: 2.2997 - mse: 9.5506\n",
"Epoch 34/1000\n",
"314/314 [==============================] - 0s 64us/sample - loss: 9.1293 - mae: 2.2197 - mse: 9.1293\n",
"Epoch 35/1000\n",
"314/314 [==============================] - 0s 55us/sample - loss: 9.4094 - mae: 2.2649 - mse: 9.4094\n",
"Epoch 36/1000\n",
"314/314 [==============================] - 0s 62us/sample - loss: 9.0601 - mae: 2.2059 - mse: 9.0601\n",
"Epoch 37/1000\n",
"314/314 [==============================] - 0s 64us/sample - loss: 8.7410 - mae: 2.1689 - mse: 8.7410\n",
"Epoch 38/1000\n",
"314/314 [==============================] - 0s 60us/sample - loss: 8.7695 - mae: 2.1735 - mse: 8.7695\n",
"Epoch 39/1000\n",
"314/314 [==============================] - 0s 56us/sample - loss: 8.4904 - mae: 2.1514 - mse: 8.4904\n",
"Epoch 40/1000\n",
"314/314 [==============================] - 0s 59us/sample - loss: 8.6032 - mae: 2.1546 - mse: 8.6032\n",
"Epoch 41/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 8.3953 - mae: 2.1234 - mse: 8.3953\n",
"Epoch 42/1000\n",
"314/314 [==============================] - 0s 55us/sample - loss: 8.4455 - mae: 2.1295 - mse: 8.4455\n",
"Epoch 43/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 8.3783 - mae: 2.1127 - mse: 8.3783\n",
"Epoch 44/1000\n",
"314/314 [==============================] - 0s 55us/sample - loss: 8.2581 - mae: 2.0840 - mse: 8.2582\n",
"Epoch 45/1000\n",
"314/314 [==============================] - 0s 63us/sample - loss: 8.1526 - mae: 2.0891 - mse: 8.1526\n",
"Epoch 46/1000\n",
"314/314 [==============================] - 0s 66us/sample - loss: 8.0769 - mae: 2.0710 - mse: 8.0769\n",
"Epoch 47/1000\n",
"314/314 [==============================] - 0s 60us/sample - loss: 7.9343 - mae: 2.0748 - mse: 7.9343\n",
"Epoch 48/1000\n",
"314/314 [==============================] - 0s 56us/sample - loss: 7.9564 - mae: 2.0374 - mse: 7.9564\n",
"Epoch 49/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 7.8619 - mae: 2.0614 - mse: 7.8619\n",
"Epoch 50/1000\n",
"314/314 [==============================] - ETA: 0s - loss: 9.3458 - mae: 2.3700 - mse: 9.345 - 0s 56us/sample - loss: 7.6290 - mae: 2.0272 - mse: 7.6290\n",
"Epoch 51/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 7.7290 - mae: 2.0406 - mse: 7.7290\n",
"Epoch 52/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 7.8052 - mae: 2.0272 - mse: 7.8052\n",
"Epoch 53/1000\n",
"314/314 [==============================] - 0s 59us/sample - loss: 7.6580 - mae: 2.0258 - mse: 7.6580\n",
"Epoch 54/1000\n",
"314/314 [==============================] - 0s 62us/sample - loss: 7.7485 - mae: 2.0088 - mse: 7.7485\n",
"Epoch 55/1000\n",
"314/314 [==============================] - 0s 58us/sample - loss: 7.4744 - mae: 1.9804 - mse: 7.4744\n",
"Epoch 56/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 7.4426 - mae: 1.9980 - mse: 7.4426\n",
"Epoch 57/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 7.3071 - mae: 1.9490 - mse: 7.3071\n",
"Epoch 58/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 7.5041 - mae: 2.0069 - mse: 7.5041\n",
"Epoch 59/1000\n",
"314/314 [==============================] - 0s 56us/sample - loss: 7.5007 - mae: 1.9827 - mse: 7.5007\n",
"Epoch 60/1000\n",
"314/314 [==============================] - 0s 62us/sample - loss: 7.3636 - mae: 1.9668 - mse: 7.3636\n",
"Epoch 61/1000\n",
"314/314 [==============================] - 0s 62us/sample - loss: 7.5914 - mae: 1.9862 - mse: 7.5914\n",
"Epoch 62/1000\n",
"314/314 [==============================] - 0s 63us/sample - loss: 7.3816 - mae: 1.9625 - mse: 7.3816\n",
"Epoch 63/1000\n",
"314/314 [==============================] - 0s 54us/sample - loss: 7.3483 - mae: 1.9722 - mse: 7.3483\n",
"Epoch 64/1000\n",
"314/314 [==============================] - 0s 64us/sample - loss: 7.2078 - mae: 1.9329 - mse: 7.2078\n",
"Epoch 65/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 7.3021 - mae: 1.9711 - mse: 7.3021\n",
"Epoch 66/1000\n",
"314/314 [==============================] - 0s 56us/sample - loss: 7.4262 - mae: 1.9638 - mse: 7.4262\n",
"Epoch 67/1000\n",
"314/314 [==============================] - 0s 55us/sample - loss: 7.2357 - mae: 1.9453 - mse: 7.2357\n",
"Epoch 68/1000\n",
"314/314 [==============================] - 0s 56us/sample - loss: 7.1882 - mae: 1.9233 - mse: 7.1882\n",
"Epoch 69/1000\n",
"314/314 [==============================] - 0s 55us/sample - loss: 7.1541 - mae: 1.9313 - mse: 7.1541\n",
"Epoch 70/1000\n",
"314/314 [==============================] - 0s 61us/sample - loss: 7.1842 - mae: 1.9248 - mse: 7.1842\n",
"Epoch 71/1000\n",
"314/314 [==============================] - 0s 62us/sample - loss: 7.1776 - mae: 1.9138 - mse: 7.1776\n",
"Epoch 72/1000\n",
"314/314 [==============================] - 0s 62us/sample - loss: 7.0900 - mae: 1.9293 - mse: 7.0900\n",
"Epoch 73/1000\n",
"314/314 [==============================] - 0s 57us/sample - loss: 7.2542 - mae: 1.9187 - mse: 7.2542\n",
"Epoch 74/1000\n",
"314/314 [==============================] - 0s 63us/sample - loss: 7.1510 - mae: 1.9227 - mse: 7.1510\n",
"Epoch 75/1000\n",
"314/314 [==============================] - ETA: 0s - loss: 5.5019 - mae: 1.7552 - mse: 5.501 - 0s 63us/sample - loss: 7.0345 - mae: 1.9029 - mse: 7.0345\n",
"Epoch 76/1000\n",
"314/314 [==============================] - 0s 59us/sample - loss: 6.9480 - mae: 1.8899 - mse: 6.9480\n",
"Epoch 77/1000\n",
"314/314 [==============================] - 0s 55us/sample - loss: 7.1362 - mae: 1.9281 - mse: 7.1362\n",
"Epoch 78/1000\n",
"314/314 [==============================] - 0s 55us/sample - loss: 7.0816 - mae: 1.9190 - mse: 7.0816\n",
"Epoch 79/1000\n",
"314/314 [==============================] - 0s 54us/sample - loss: 7.0792 - mae: 1.9059 - mse: 7.0792\n",
"Epoch 80/1000\n",
"314/314 [==============================] - 0s 61us/sample - loss: 6.8694 - mae: 1.9024 - mse: 6.8694\n",
"Epoch 81/1000\n",
"314/314 [==============================] - 0s 61us/sample - loss: 6.8832 - mae: 1.9102 - mse: 6.8832\n",
"Epoch 82/1000\n",
"314/314 [==============================] - 0s 58us/sample - loss: 6.8986 - mae: 1.9037 - mse: 6.8986\n",
"Epoch 83/1000\n",
"314/314 [==============================] - 0s 65us/sample - loss: 6.8722 - mae: 1.9188 - mse: 6.8722\n",
"Epoch 84/1000\n",
"314/314 [==============================] - 0s 55us/sample - loss: 7.0697 - mae: 1.9051 - mse: 7.0697\n",
"Epoch 85/1000\n",
"314/314 [==============================] - 0s 59us/sample - loss: 6.8875 - mae: 1.9075 - mse: 6.8875\n",
"Epoch 86/1000\n",
"314/314 [==============================] - 0s 61us/sample - loss: 6.9075 - mae: 1.9014 - mse: 6.9075\n",
"Epoch 87/1000\n",
"314/314 [==============================] - 0s 58us/sample - loss: 6.9516 - mae: 1.8844 - mse: 6.9516\n",
"Epoch 88/1000\n",
"314/314 [==============================] - 0s 56us/sample - loss: 6.8804 - mae: 1.8884 - mse: 6.8804\n",
"Epoch 89/1000\n",
"314/314 [==============================] - 0s 58us/sample - loss: 6.9503 - mae: 1.8655 - mse: 6.9503\n",
"Epoch 90/1000\n",
"314/314 [==============================] - 0s 59us/sample - loss: 7.0544 - mae: 1.8798 - mse: 7.0544\n"
]
}
],
"source": [
"EPOCHS = 1000\n",
"early_stop = tf.keras.callbacks.EarlyStopping(monitor='mse', patience=10) \n",
"shap_history = shap_model.fit( normed_train_data, train_labels, callbacks=[early_stop], epochs=EPOCHS, verbose=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use KMeans to Summarize Dataset"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"df_train_normed_summary = shap.kmeans(normed_train_data.values, 25)\n"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"# Instantiate an explainer with the model predictions and training data summary\n",
"explainer = shap.KernelExplainer(shap_model.predict, df_train_normed_summary)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\n!pip install ipywidgets\\n!jupyter nbextension enable --py widgetsnbextension\\n'"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# may need to install ipywidgets to see status visualization\n",
"'''\n",
"!pip install ipywidgets\n",
"!jupyter nbextension enable --py widgetsnbextension\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "169d9eaf3aa84c668b8a2610a14b2049",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=314.0), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"l1_reg=\"auto\" is deprecated and in the next version (v0.29) the behavior will change from a conditional use of AIC to simply \"num_features(10)\"!\n"
]
}
],
"source": [
"# Extract Shapley values from the explainer\n",
"shap_values = explainer.shap_values(normed_train_data.values)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Summarize the Shapley values in a plot\n",
"shap.summary_plot(shap_values[0], normed_train_data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"INSTANCE_NUM = 0\n",
"shap.force_plot(explainer.expected_value[0], shap_values[0][INSTANCE_NUM], normed_train_data.iloc[INSTANCE_NUM,:])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Plot the SHAP values for multiple instances\n",
"NUM_ROWS = 10\n",
"shap.force_plot(explainer.expected_value[0], shap_values[0][0:NUM_ROWS], normed_train_data.iloc[0:NUM_ROWS])"
]
},
{
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}