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target_encoding.ipynb
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{
"cell_type": "markdown",
"source": [
"# Target Encoding"
],
"metadata": {
"id": "Id3wmlMua0T1"
}
},
{
"cell_type": "markdown",
"source": [
"* https://medium.com/@heitornunesrosa/o-que-%C3%A9-target-encoding-e-como-aplic%C3%A1-lo-85996e1bf00d\n",
"* https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.TargetEncoder.html\n",
"* https://github.com/brusangues/papers/blob/main/Catboost/micci-barreca2001.pdf\n",
"* https://catboost.ai/docs/en/concepts/algorithm-main-stages_cat-to-numberic\n",
"* https://github.com/brusangues/papers/blob/main/Catboost/Transforming%20categorical%20features%20to%20numerical%20features%20_%20CatBoost.pdf\n",
"* https://contrib.scikit-learn.org/category_encoders/catboost.html"
],
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{
"cell_type": "code",
"source": [
"%pip install category_encoders catboost -q"
],
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"id": "PPXGNFFfbYIB"
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"execution_count": 1,
"outputs": []
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"source": [
"import pandas as pd\n",
"import numpy as np\n",
"pd.set_option('future.no_silent_downcasting', True)\n",
"\n",
"# Create a classification dataset\n",
"data_classification = {'city': ['New York', 'London', 'Paris', 'New York', 'London', 'Paris', 'New York', 'London', 'New York', 'London', 'New York', 'London'],\n",
" 'churn': [0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1]}\n",
"df_clf = pd.DataFrame(data_classification)\n",
"\n",
"# Create a regression dataset\n",
"data_regression = {'product': ['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B'],\n",
" 'price': [10.5, 20.2, 15.0, 11.0, 19.5, 16.0, 10.0, 21.0]}\n",
"df_reg = pd.DataFrame(data_regression)\n",
"\n",
"display(df_clf)\n",
"display(df_reg)"
],
"execution_count": 2,
"outputs": [
{
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"data": {
"text/plain": [
" city churn\n",
"0 New York 0\n",
"1 London 1\n",
"2 Paris 0\n",
"3 New York 1\n",
"4 London 0\n",
"5 Paris 1\n",
"6 New York 0\n",
"7 London 1\n",
"8 New York 1\n",
"9 London 1\n",
"10 New York 1\n",
"11 London 1"
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" product price\n",
"0 A 10.5\n",
"1 B 20.2\n",
"2 C 15.0\n",
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df_reg",
"summary": "{\n \"name\": \"df_reg\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"product\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"A\",\n \"B\",\n \"C\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.539666444637913,\n \"min\": 10.0,\n \"max\": 21.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 20.2,\n 16.0,\n 10.5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"print(\"Média do target\", df_clf.churn.mean())\n",
"print(\"Média por categoria\\n\", df_clf.groupby(\"city\")[[\"churn\"]].mean())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3SkBEFm5YCNT",
"outputId": "f8e1a06d-f41a-459a-b8d5-bf8abf3281dc"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Média do target 0.6666666666666666\n",
"Média por categoria\n",
" churn\n",
"city \n",
"London 0.8\n",
"New York 0.6\n",
"Paris 0.5\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"from sklearn.preprocessing import TargetEncoder\n",
"\n",
"# Fazendo dados duplicados para burlar a validação cruzada obrigatória\n",
"df_clf_double = pd.concat([df_clf,df_clf]).reset_index(drop=True)\n",
"\n",
"# Parâmetro de mistura = 0 -> peso total para a média a posteriori (média local)\n",
"enc_smooth0 = TargetEncoder(smooth=0, cv=2, shuffle=False)\n",
"X_trans_double = enc_smooth0.fit_transform(df_clf_double[[\"city\"]], df_clf_double.churn)\n",
"df_clf[\"enc_smooth0\"] = X_trans_double[:df_clf.shape[0]]\n",
"\n",
"# Parâmetro de mistura = +inf -> peso total para a média a priori (média global)\n",
"enc_smooth1 = TargetEncoder(smooth=1_000_000_000, cv=2, shuffle=False)\n",
"X_trans_double = enc_smooth1.fit_transform(df_clf_double[[\"city\"]], df_clf_double.churn)\n",
"df_clf[\"enc_smooth1\"] = X_trans_double[:df_clf.shape[0]]\n",
"\n",
"# Parâmetro de mistura = auto -> atribuição seguindo a fórmula teórica\n",
"enc_smooth_auto = TargetEncoder(smooth=\"auto\", cv=2, shuffle=False)\n",
"X_trans_double = enc_smooth_auto.fit_transform(df_clf_double[[\"city\"]], df_clf_double.churn)\n",
"df_clf[\"enc_smooth_auto\"] = X_trans_double[:df_clf.shape[0]]\n",
"\n",
"df_clf"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 426
},
"id": "xz1b0Cb-bZd9",
"outputId": "fac3f129-cc62-4176-bb4e-25b18c4b2328"
},
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" city churn enc_smooth0 enc_smooth1 enc_smooth_auto\n",
"0 New York 0 0.6 0.666667 0.611842\n",
"1 London 1 0.8 0.666667 0.783217\n",
"2 Paris 0 0.5 0.666667 0.560000\n",
"3 New York 1 0.6 0.666667 0.611842\n",
"4 London 0 0.8 0.666667 0.783217\n",
"5 Paris 1 0.5 0.666667 0.560000\n",
"6 New York 0 0.6 0.666667 0.611842\n",
"7 London 1 0.8 0.666667 0.783217\n",
"8 New York 1 0.6 0.666667 0.611842\n",
"9 London 1 0.8 0.666667 0.783217\n",
"10 New York 1 0.6 0.666667 0.611842\n",
"11 London 1 0.8 0.666667 0.783217"
],
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"<style scoped>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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"summary": "{\n \"name\": \"df_clf\",\n \"rows\": 12,\n \"fields\": [\n {\n \"column\": \"city\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"New York\",\n \"London\",\n \"Paris\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"churn\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_smooth0\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.12309149097933277,\n \"min\": 0.5,\n \"max\": 0.8,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.6,\n 0.8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_smooth1\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5.149286359812627e-10,\n \"min\": 0.6666666663333333,\n \"max\": 0.6666666673333332,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.6666666673333332,\n 0.6666666663333333\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_smooth_auto\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.097676234197722,\n \"min\": 0.56,\n \"max\": 0.7832167832167832,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.6118421052631579,\n 0.7832167832167832\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
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},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"source": [
"df_clf.drop_duplicates([\"city\"])"
],
"metadata": {
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"base_uri": "https://localhost:8080/",
"height": 143
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"id": "QlqtnLrbchL3",
"outputId": "230b61c2-f9c3-4bdb-c9f2-6028ac4bf867"
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"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" city churn enc_smooth0 enc_smooth1 enc_smooth_auto\n",
"0 New York 0 0.6 0.666667 0.611842\n",
"1 London 1 0.8 0.666667 0.783217\n",
"2 Paris 0 0.5 0.666667 0.560000"
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"metadata": {},
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{
"cell_type": "code",
"source": [],
"metadata": {
"id": "4BAbfszQhoBq"
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"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def target_encoding(df_, target_mean, alpha=\"auto\", k = 20, f = 100, target_col=\"churn\"):\n",
" if alpha==\"auto\":\n",
" ni = df_.shape[0]\n",
" alpha = 1 / (1 + np.exp(-(ni - k)/f))\n",
" encode = alpha * df_[target_col].mean() + (1-alpha) * target_mean\n",
" return encode\n",
"\n",
"alpha=1 # \"auto\"\n",
"target_mean = df_clf.churn.mean()\n",
"\n",
"encoding = df_clf.groupby(\"city\").apply(lambda x: target_encoding(x, target_mean, alpha=alpha), include_groups=False)\n",
"df_clf['enc_smooth_manual'] = df_clf[\"city\"].map(encoding.to_dict())\n",
"df_clf"
],
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"height": 426
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"id": "Y1jQROJVfEEv",
"outputId": "e4cc3882-e59b-41b4-b78d-20b6fd1438b1"
},
"execution_count": 6,
"outputs": [
{
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" city churn enc_smooth0 enc_smooth1 enc_smooth_auto \\\n",
"0 New York 0 0.6 0.666667 0.611842 \n",
"1 London 1 0.8 0.666667 0.783217 \n",
"2 Paris 0 0.5 0.666667 0.560000 \n",
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"9 London 1 0.8 0.666667 0.783217 \n",
"10 New York 1 0.6 0.666667 0.611842 \n",
"11 London 1 0.8 0.666667 0.783217 \n",
"\n",
" enc_smooth_manual \n",
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"type": "dataframe",
"variable_name": "df_clf",
"summary": "{\n \"name\": \"df_clf\",\n \"rows\": 12,\n \"fields\": [\n {\n \"column\": \"city\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"New York\",\n \"London\",\n \"Paris\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"churn\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_smooth0\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.12309149097933277,\n \"min\": 0.5,\n \"max\": 0.8,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.6,\n 0.8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_smooth1\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5.149286359812627e-10,\n \"min\": 0.6666666663333333,\n \"max\": 0.6666666673333332,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.6666666673333332,\n 0.6666666663333333\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_smooth_auto\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.097676234197722,\n \"min\": 0.56,\n \"max\": 0.7832167832167832,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.6118421052631579,\n 0.7832167832167832\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_smooth_manual\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.12309149097933277,\n \"min\": 0.5,\n \"max\": 0.8,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.6,\n 0.8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"source": [
"print(enc_smooth_auto.target_type_)\n",
"print(enc_smooth_auto.target_mean_)\n",
"print(enc_smooth_auto.get_params())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Hw4QHwnpZ80-",
"outputId": "0538ccee-df38-4c6a-e4e4-576a636d1d41"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"binary\n",
"0.6666666666666666\n",
"{'categories': 'auto', 'cv': 2, 'random_state': None, 'shuffle': False, 'smooth': 'auto', 'target_type': 'auto'}\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from category_encoders.cat_boost import CatBoostEncoder\n",
"\n",
"cbe = CatBoostEncoder()\n",
"\n",
"df_clf['enc_catboost'] = cbe.fit_transform(df_clf[['city']], df_clf['churn'])\n",
"\n",
"df_clf"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 426
},
"id": "9W13_R2pbuCE",
"outputId": "c9ee987a-aef9-45fd-a7bd-f37997973a27"
},
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" city churn enc_smooth0 enc_smooth1 enc_smooth_auto \\\n",
"0 New York 0 0.6 0.666667 0.611842 \n",
"1 London 1 0.8 0.666667 0.783217 \n",
"2 Paris 0 0.5 0.666667 0.560000 \n",
"3 New York 1 0.6 0.666667 0.611842 \n",
"4 London 0 0.8 0.666667 0.783217 \n",
"5 Paris 1 0.5 0.666667 0.560000 \n",
"6 New York 0 0.6 0.666667 0.611842 \n",
"7 London 1 0.8 0.666667 0.783217 \n",
"8 New York 1 0.6 0.666667 0.611842 \n",
"9 London 1 0.8 0.666667 0.783217 \n",
"10 New York 1 0.6 0.666667 0.611842 \n",
"11 London 1 0.8 0.666667 0.783217 \n",
"\n",
" enc_smooth_manual enc_catboost \n",
"0 0.6 0.666667 \n",
"1 0.8 0.666667 \n",
"2 0.5 0.666667 \n",
"3 0.6 0.333333 \n",
"4 0.8 0.833333 \n",
"5 0.5 0.333333 \n",
"6 0.6 0.555556 \n",
"7 0.8 0.555556 \n",
"8 0.6 0.416667 \n",
"9 0.8 0.666667 \n",
"10 0.6 0.533333 \n",
"11 0.8 0.733333 "
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" <td>New York</td>\n",
" <td>0</td>\n",
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" <td>0.666667</td>\n",
" <td>0.611842</td>\n",
" <td>0.6</td>\n",
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" <td>0.8</td>\n",
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" <td>New York</td>\n",
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" <td>0.611842</td>\n",
" <td>0.6</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>London</td>\n",
" <td>0</td>\n",
" <td>0.8</td>\n",
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" <td>0.783217</td>\n",
" <td>0.8</td>\n",
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" <td>Paris</td>\n",
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" <td>0.5</td>\n",
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" <td>0.560000</td>\n",
" <td>0.5</td>\n",
" <td>0.333333</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>New York</td>\n",
" <td>0</td>\n",
" <td>0.6</td>\n",
" <td>0.666667</td>\n",
" <td>0.611842</td>\n",
" <td>0.6</td>\n",
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" <tr>\n",
" <th>7</th>\n",
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" <td>1</td>\n",
" <td>0.8</td>\n",
" <td>0.666667</td>\n",
" <td>0.783217</td>\n",
" <td>0.8</td>\n",
" <td>0.555556</td>\n",
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" <tr>\n",
" <th>8</th>\n",
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" <td>1</td>\n",
" <td>0.6</td>\n",
" <td>0.666667</td>\n",
" <td>0.611842</td>\n",
" <td>0.6</td>\n",
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" <tr>\n",
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" <td>1</td>\n",
" <td>0.8</td>\n",
" <td>0.666667</td>\n",
" <td>0.783217</td>\n",
" <td>0.8</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
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" <td>1</td>\n",
" <td>0.6</td>\n",
" <td>0.666667</td>\n",
" <td>0.611842</td>\n",
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" <td>0.8</td>\n",
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" <td>0.783217</td>\n",
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"type": "dataframe",
"variable_name": "df_clf",
"summary": "{\n \"name\": \"df_clf\",\n \"rows\": 12,\n \"fields\": [\n {\n \"column\": \"city\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"New York\",\n \"London\",\n \"Paris\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"churn\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_smooth0\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.12309149097933277,\n \"min\": 0.5,\n \"max\": 0.8,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.6,\n 0.8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_smooth1\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5.149286359812627e-10,\n \"min\": 0.6666666663333333,\n \"max\": 0.6666666673333332,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.6666666673333332,\n 0.6666666663333333\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_smooth_auto\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.097676234197722,\n \"min\": 0.56,\n \"max\": 0.7832167832167832,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.6118421052631579,\n 0.7832167832167832\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_smooth_manual\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.12309149097933277,\n \"min\": 0.5,\n \"max\": 0.8,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.6,\n 0.8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_catboost\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.15651684098335997,\n \"min\": 0.3333333333333333,\n \"max\": 0.8333333333333333,\n \"num_unique_values\": 7,\n \"samples\": [\n 0.6666666666666666,\n 0.3333333333333333\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "UMb_bL1FliCG"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# CBE MANUAL WORK IN PROGRESS\n",
"\n",
"# All categorical feature values are transformed to numerical using the following formula:\n",
"# avg_target=(countInClass+prior)/totalCount+1\n",
"# countInClass is how many times the label value was equal to 1 for objects with the current categorical feature value.\n",
"# prior is the preliminary value for the numerator. It is determined by the starting parameters.\n",
"# totalCount is the total number of objects (up to the current one) that have a categorical feature value matching the current one.\n",
"\n",
"cbe_manual = []\n",
"prior = 1\n",
"counts_class = {k:0 for k in df_clf.city.unique()}\n",
"counts_total = {k:0 for k in df_clf.city.unique()}\n",
"\n",
"for i, row in df_clf.iterrows():\n",
" if row.churn==1:\n",
" counts_class[row.city] += 1\n",
" counts_total[row.city] += 1\n",
"\n",
" avg_target = (counts_class[row.city] + prior) / (counts_total[row.city] + 1)\n",
" cbe_manual.append(avg_target)\n",
"\n",
"\n",
"df_clf['enc_catboost_manual'] = cbe_manual\n",
"\n",
"df_clf"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 426
},
"id": "rPUaj7DEk20P",
"outputId": "a3eb891b-ef6a-479f-d2b1-8f36626c8c9e"
},
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" city churn enc_smooth0 enc_smooth1 enc_smooth_auto \\\n",
"0 New York 0 0.6 0.666667 0.611842 \n",
"1 London 1 0.8 0.666667 0.783217 \n",
"2 Paris 0 0.5 0.666667 0.560000 \n",
"3 New York 1 0.6 0.666667 0.611842 \n",
"4 London 0 0.8 0.666667 0.783217 \n",
"5 Paris 1 0.5 0.666667 0.560000 \n",
"6 New York 0 0.6 0.666667 0.611842 \n",
"7 London 1 0.8 0.666667 0.783217 \n",
"8 New York 1 0.6 0.666667 0.611842 \n",
"9 London 1 0.8 0.666667 0.783217 \n",
"10 New York 1 0.6 0.666667 0.611842 \n",
"11 London 1 0.8 0.666667 0.783217 \n",
"\n",
" enc_smooth_manual enc_catboost enc_catboost_manual \n",
"0 0.6 0.666667 0.500000 \n",
"1 0.8 0.666667 1.000000 \n",
"2 0.5 0.666667 0.500000 \n",
"3 0.6 0.333333 0.666667 \n",
"4 0.8 0.833333 0.666667 \n",
"5 0.5 0.333333 0.666667 \n",
"6 0.6 0.555556 0.500000 \n",
"7 0.8 0.555556 0.750000 \n",
"8 0.6 0.416667 0.600000 \n",
"9 0.8 0.666667 0.800000 \n",
"10 0.6 0.533333 0.666667 \n",
"11 0.8 0.733333 0.833333 "
],
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" <td>0.6</td>\n",
" <td>0.666667</td>\n",
" <td>0.611842</td>\n",
" <td>0.6</td>\n",
" <td>0.666667</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>London</td>\n",
" <td>1</td>\n",
" <td>0.8</td>\n",
" <td>0.666667</td>\n",
" <td>0.783217</td>\n",
" <td>0.8</td>\n",
" <td>0.666667</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Paris</td>\n",
" <td>0</td>\n",
" <td>0.5</td>\n",
" <td>0.666667</td>\n",
" <td>0.560000</td>\n",
" <td>0.5</td>\n",
" <td>0.666667</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>New York</td>\n",
" <td>1</td>\n",
" <td>0.6</td>\n",
" <td>0.666667</td>\n",
" <td>0.611842</td>\n",
" <td>0.6</td>\n",
" <td>0.333333</td>\n",
" <td>0.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>London</td>\n",
" <td>0</td>\n",
" <td>0.8</td>\n",
" <td>0.666667</td>\n",
" <td>0.783217</td>\n",
" <td>0.8</td>\n",
" <td>0.833333</td>\n",
" <td>0.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Paris</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.666667</td>\n",
" <td>0.560000</td>\n",
" <td>0.5</td>\n",
" <td>0.333333</td>\n",
" <td>0.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>New York</td>\n",
" <td>0</td>\n",
" <td>0.6</td>\n",
" <td>0.666667</td>\n",
" <td>0.611842</td>\n",
" <td>0.6</td>\n",
" <td>0.555556</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>London</td>\n",
" <td>1</td>\n",
" <td>0.8</td>\n",
" <td>0.666667</td>\n",
" <td>0.783217</td>\n",
" <td>0.8</td>\n",
" <td>0.555556</td>\n",
" <td>0.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>New York</td>\n",
" <td>1</td>\n",
" <td>0.6</td>\n",
" <td>0.666667</td>\n",
" <td>0.611842</td>\n",
" <td>0.6</td>\n",
" <td>0.416667</td>\n",
" <td>0.600000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>London</td>\n",
" <td>1</td>\n",
" <td>0.8</td>\n",
" <td>0.666667</td>\n",
" <td>0.783217</td>\n",
" <td>0.8</td>\n",
" <td>0.666667</td>\n",
" <td>0.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>New York</td>\n",
" <td>1</td>\n",
" <td>0.6</td>\n",
" <td>0.666667</td>\n",
" <td>0.611842</td>\n",
" <td>0.6</td>\n",
" <td>0.533333</td>\n",
" <td>0.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>London</td>\n",
" <td>1</td>\n",
" <td>0.8</td>\n",
" <td>0.666667</td>\n",
" <td>0.783217</td>\n",
" <td>0.8</td>\n",
" <td>0.733333</td>\n",
" <td>0.833333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"\n",
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df_clf",
"summary": "{\n \"name\": \"df_clf\",\n \"rows\": 12,\n \"fields\": [\n {\n \"column\": \"city\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"New York\",\n \"London\",\n \"Paris\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"churn\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_smooth0\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.12309149097933277,\n \"min\": 0.5,\n \"max\": 0.8,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.6,\n 0.8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_smooth1\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5.149286359812627e-10,\n \"min\": 0.6666666663333333,\n \"max\": 0.6666666673333332,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.6666666673333332,\n 0.6666666663333333\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_smooth_auto\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.097676234197722,\n \"min\": 0.56,\n \"max\": 0.7832167832167832,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.6118421052631579,\n 0.7832167832167832\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_smooth_manual\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.12309149097933277,\n \"min\": 0.5,\n \"max\": 0.8,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.6,\n 0.8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_catboost\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.15651684098335997,\n \"min\": 0.3333333333333333,\n \"max\": 0.8333333333333333,\n \"num_unique_values\": 7,\n \"samples\": [\n 0.6666666666666666,\n 0.3333333333333333\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"enc_catboost_manual\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.1506090329564625,\n \"min\": 0.5,\n \"max\": 1.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 0.5,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 9
}
]
}
]
}
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