notebooks/1.2_rf_lastname.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "43bdd7b0-bff8-428d-9025-0593bd0330d5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import joblib\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f9bb41e-594e-43a6-ab47-cb2dfc35f40f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('./data/fl_2022_LastName_train.csv.gz')\n",
    "val_df = pd.read_csv('./data/fl_2022_LastName_val.csv.gz')\n",
    "test_df = pd.read_csv('./data/fl_2022_LastName_test.csv.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08cc4254-d07e-4b01-9e55-14ca25bdc558",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1079199, 4)\n",
      "(134900, 4)\n",
      "(134900, 4)\n"
     ]
    }
   ],
   "source": [
    "print(train_df.shape)\n",
    "print(val_df.shape)\n",
    "print(test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "863a006e-f082-49c3-9eca-c4267997bfa7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df = train_df.dropna()\n",
    "val_df = val_df.dropna()\n",
    "test_df = test_df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e2ed379-51d3-445d-995e-258c724d3abb",
   "metadata": {},
   "source": [
    "## Extract features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56a1d629-37ea-435f-b99e-f48e285335d6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer(analyzer='char', ngram_range=(1, 2), lowercase=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "accd0e12-5cb2-4f64-b46e-fd0e65b4843c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train = vectorizer.fit_transform(train_df['name_last'])\n",
    "X_val = vectorizer.transform(val_df['name_last'])\n",
    "X_test = vectorizer.transform(test_df['name_last'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d18ba028-c270-49fc-8921-18b996754fa5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1079191, 1588)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "84e32c72-e1c4-48dc-bda2-6348b9114e5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134896, 1588)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0af4b540-2f0b-4af3-8e48-931c105419af",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134898, 1588)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7078aec6-e9ef-4393-9086-e6c6b919a8a6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' ', \" '\", ' -', ..., 'zx', 'zy', 'zz'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8f8729a2-b6f0-4265-abbd-d9878879f14a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1588"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vectorizer.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "19714c3f-3aa9-4e2f-8145-1aeda563f13c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "vocab = vectorizer.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "550526c7-8ac1-4e4f-b1ca-3922832c6119",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_train = train_df.race_code\n",
    "y_val = val_df.race_code\n",
    "y_test = test_df.race_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b0c0894-2832-4d63-a09b-0063c7d06207",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c5747cde-b751-4037-9012-95ec8e7b4566",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Create a Gaussian Classifier\n",
    "clf = RandomForestClassifier(criterion = 'entropy', random_state=42, verbose=6, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "27b2c2e0-561e-42c9-965f-72cfcfd2caa8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1079191, 1588)\n",
      "(134898, 1588)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4a954e6-adb7-4ed1-a998-ff259bb63d59",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eaf206d-5323-4b62-b449-6ee4df96390c",
   "metadata": {},
   "source": [
    "## Doing it for 1 million with 100 iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5adcf7e6-ee3d-4e57-b008-e0bfaab740ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 10 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 100\n",
      "building tree 2 of 100\n",
      "building tree 3 of 100\n",
      "building tree 4 of 100\n",
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    {
     "name": "stderr",
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     "text": [
      "[Parallel(n_jobs=-1)]: Done  30 tasks      | elapsed: 42.9min\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
    "\n",
    "# 1 million\n",
    "samples = 100 * 100 * 100\n",
    "# 1 million records took 1h 11min 16s\n",
    "\n",
    "clf.fit(X_train[:samples], y_train[:samples])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0e02b4b-c6fb-45db-b25d-4a8ca6b1b1d9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done  30 tasks      | elapsed:    9.3s\n",
      "[Parallel(n_jobs=10)]: Done  98 out of 100 | elapsed:   30.8s remaining:    0.6s\n",
      "[Parallel(n_jobs=10)]: Done 100 out of 100 | elapsed:   30.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.5578890404459732\n",
      "CPU times: user 3min 4s, sys: 30.5 s, total: 3min 35s\n",
      "Wall time: 32.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "y_pred = clf.predict(X_val)\n",
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Test Accuracy:\", metrics.accuracy_score(y_val, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "706b6bbd-7bf7-4f7f-b3ca-42f425ee8da0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done  30 tasks      | elapsed:    0.7s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.5566872748298715\n",
      "CPU times: user 21.7 s, sys: 388 ms, total: 22.1 s\n",
      "Wall time: 3.04 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Done  98 out of 100 | elapsed:    2.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done 100 out of 100 | elapsed:    2.4s finished\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "y_pred = clf.predict(X_test)\n",
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Test Accuracy:\", metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2899fe5b-1699-444d-988a-981a7053940f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/rf_vec_lastname_1M.joblib']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(vectorizer, \"models/rf_vec_lastname_1M.joblib\", compress=3)  # compression is ON!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7d836a71-0053-4159-819e-b78f7fbbd27c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/rf_model_lastname_1M.joblib']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(clf, \"models/rf_model_lastname_1M.joblib\", compress=3)  # compression is ON!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e273dbc0-f400-4915-9f73-fcf0532c0155",
   "metadata": {},
   "source": [
    "## Doing it for 2 million with 100 iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "76f1c55a-8ff1-4286-8670-699c2bba71d5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 10 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 100\n",
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      "CPU times: user 1d 2h 19min 34s, sys: 23min 28s, total: 1d 2h 43min 2s\n",
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    },
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     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_jobs=-1, random_state=42,\n",
       "                       verbose=6)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_jobs=-1, random_state=42,\n",
       "                       verbose=6)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', n_jobs=-1, random_state=42,\n",
       "                       verbose=6)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
    "clf = RandomForestClassifier(criterion = 'entropy', random_state=42, verbose=6, n_jobs=-1)\n",
    "\n",
    "# 2 million\n",
    "samples = 2 * 100 * 100 * 100\n",
    "# 1 million records took 2h 10min 13s\n",
    "# 2 million records took 3h 50 mins\n",
    "# 8 million records will take ~17 hours\n",
    "\n",
    "clf.fit(X_train[:samples], y_train[:samples])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d915e779-d055-4c74-b129-5864ff3dd982",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done  30 tasks      | elapsed:   25.6s\n",
      "[Parallel(n_jobs=10)]: Done  98 out of 100 | elapsed:   37.6s remaining:    0.8s\n",
      "[Parallel(n_jobs=10)]: Done 100 out of 100 | elapsed:   37.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Accuracy: 0.5544567666943423\n",
      "CPU times: user 4min 40s, sys: 17.8 s, total: 4min 58s\n",
      "Wall time: 46.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "y_pred = clf.predict(X_val)\n",
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Val Accuracy:\", metrics.accuracy_score(y_val, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "574338bf-b610-4619-bd23-ddc2eec5c899",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done  30 tasks      | elapsed:    0.9s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.5539296357247698\n",
      "CPU times: user 20.9 s, sys: 108 ms, total: 21 s\n",
      "Wall time: 2.83 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Done  98 out of 100 | elapsed:    2.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=10)]: Done 100 out of 100 | elapsed:    2.2s finished\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "y_pred = clf.predict(X_test)\n",
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Test Accuracy:\", metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ce7677d6-3723-4d2b-9e88-823ed900dc6b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/rf_vec_lastname_2M.joblib']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(vectorizer, \"models/rf_vec_lastname_2M.joblib\", compress=3)  # compression is ON!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ba7d6ce8-ed66-4d5e-86a0-f75a66c35bc2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/rf_model_lastname_2M.joblib']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(clf, \"models/rf_model_lastname_2M.joblib\", compress=3)  # compression is ON!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f97bc2d-13a9-430d-970d-4929d539f8f5",
   "metadata": {},
   "source": [
    "# Now lets do for the whole train dataset with 20 iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f88669a8-23d8-473f-a377-287a26139330",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 10 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 20\n",
      "building tree 2 of 20\n",
      "building tree 3 of 20\n",
      "building tree 4 of 20\n",
      "building tree 5 of 20\n",
      "building tree 6 of 20\n",
      "building tree 7 of 20\n",
      "building tree 8 of 20\n",
      "building tree 9 of 20\n",
      "building tree 10 of 20\n",
      "building tree 11 of 20\n",
      "building tree 12 of 20\n",
      "building tree 13 of 20\n",
      "building tree 14 of 20\n",
      "building tree 15 of 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   5 out of  20 | elapsed: 15.5min remaining: 46.4min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 16 of 20\n",
      "building tree 17 of 20\n",
      "building tree 18 of 20\n",
      "building tree 19 of 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   9 out of  20 | elapsed: 15.8min remaining: 19.3min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 20 of 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  13 out of  20 | elapsed: 31.0min remaining: 16.7min\n",
      "[Parallel(n_jobs=-1)]: Done  17 out of  20 | elapsed: 31.5min remaining:  5.6min\n",
      "[Parallel(n_jobs=-1)]: Done  20 out of  20 | elapsed: 31.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5h 7min 22s, sys: 3min 50s, total: 5h 11min 13s\n",
      "Wall time: 31min 55s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=20, n_jobs=-1,\n",
       "                       random_state=42, verbose=6)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=20, n_jobs=-1,\n",
       "                       random_state=42, verbose=6)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', n_estimators=20, n_jobs=-1,\n",
       "                       random_state=42, verbose=6)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=20, criterion = 'entropy', random_state=42, verbose=6, n_jobs=-1)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3e216450-1c30-45cc-85c5-833c05fb2f36",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   5 out of  20 | elapsed:    1.3s remaining:    4.0s\n",
      "[Parallel(n_jobs=10)]: Done   9 out of  20 | elapsed:    1.7s remaining:    2.1s\n",
      "[Parallel(n_jobs=10)]: Done  13 out of  20 | elapsed:    2.5s remaining:    1.3s\n",
      "[Parallel(n_jobs=10)]: Done  17 out of  20 | elapsed:    3.4s remaining:    0.6s\n",
      "[Parallel(n_jobs=10)]: Done  20 out of  20 | elapsed:    4.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Accuracy: 0.5492156920887202\n",
      "CPU times: user 23 s, sys: 641 ms, total: 23.6 s\n",
      "Wall time: 5.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "y_pred = clf.predict(X_val)\n",
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Val Accuracy:\", metrics.accuracy_score(y_val, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b2a64a28-7aae-4156-bb57-76db1774680b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   5 out of  20 | elapsed:    0.2s remaining:    0.7s\n",
      "[Parallel(n_jobs=10)]: Done   9 out of  20 | elapsed:    0.3s remaining:    0.4s\n",
      "[Parallel(n_jobs=10)]: Done  13 out of  20 | elapsed:    0.4s remaining:    0.2s\n",
      "[Parallel(n_jobs=10)]: Done  17 out of  20 | elapsed:    0.5s remaining:    0.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.5484514225563017\n",
      "CPU times: user 7.48 s, sys: 249 ms, total: 7.73 s\n",
      "Wall time: 5.52 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Done  20 out of  20 | elapsed:    2.3s finished\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "y_pred = clf.predict(X_test)\n",
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Test Accuracy:\", metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fce9d2a2-df49-43fe-b477-fa51dc64f01e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/rf_vec_lastname.joblib']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(vectorizer, \"models/rf_vec_lastname.joblib\", compress=3)  # compression is ON!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e391f3db-c17b-4f01-bf1d-4317ff363572",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/rf_model_lastname.joblib']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(clf, \"models/rf_model_lastname.joblib\", compress=3)  # compression is ON!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39450453-38bd-4b6f-a792-6ecd875a106b",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "575f426e-e2c1-40bb-8c88-bf0dcec9bab8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "race_id_df = train_df[['race', 'race_code']].drop_duplicates().sort_values('race_code')\n",
    "race_to_id = dict(race_id_df.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4d186d8d-ef16-466a-9b94-132d4d79ab6f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'other': 0, 'nh_black': 1, 'nh_white': 2, 'hispanic': 3, 'asian': 4}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race_to_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "547e0fce-052e-4835-8a2a-fb8435b2b395",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "id_to_race = dict(race_id_df[['race_code', 'race']].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "324feaf6-2b00-4a27-9170-681248a23c01",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'other', 1: 'nh_black', 2: 'nh_white', 3: 'hispanic', 4: 'asian'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_to_race"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cf922af-de73-4254-93b2-a4e6e9726e6c",
   "metadata": {
    "tags": []
   },
   "source": [
    "### For 1M model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f21a2c41-faa1-49ba-b785-5a81e951d36f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: matplotlib in /home/distill1994/.local/lib/python3.10/site-packages (3.7.1)\n",
      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (23.1)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/distill1994/.local/lib/python3.10/site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/distill1994/.local/lib/python3.10/site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/distill1994/.local/lib/python3.10/site-packages (from matplotlib) (1.0.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/distill1994/.local/lib/python3.10/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/distill1994/.local/lib/python3.10/site-packages (from matplotlib) (9.3.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/distill1994/.local/lib/python3.10/site-packages (from matplotlib) (4.39.4)\n",
      "Requirement already satisfied: numpy>=1.20 in /home/distill1994/.local/lib/python3.10/site-packages (from matplotlib) (1.24.3)\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "27ace3e7-d206-448f-8fd0-8baa003bc4a0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done  30 tasks      | elapsed:    6.8s\n",
      "[Parallel(n_jobs=10)]: Done  98 out of 100 | elapsed:    9.8s remaining:    0.2s\n",
      "[Parallel(n_jobs=10)]: Done 100 out of 100 | elapsed:    9.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       asian       0.09      0.05      0.06      6867\n",
      "    hispanic       0.69      0.66      0.68     38961\n",
      "    nh_black       0.34      0.17      0.23     13726\n",
      "    nh_white       0.60      0.73      0.66     60970\n",
      "       other       0.17      0.15      0.16     14374\n",
      "\n",
      "    accuracy                           0.56    134898\n",
      "   macro avg       0.38      0.35      0.36    134898\n",
      "weighted avg       0.53      0.56      0.54    134898\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fc6fab8c250>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  \n",
    "\n",
    "model_1m = joblib.load('models/rf_model_lastname_1M.joblib')\n",
    "preds = model_1m.predict(X_test)\n",
    "predicted = [id_to_race[p] for p in preds]\n",
    "actual = [id_to_race[p] for p in y_test.values]\n",
    "\n",
    "print(classification_report(actual, predicted))\n",
    "\n",
    "cm = confusion_matrix(actual, predicted, labels=list(race_to_id.keys()))\n",
    "cmp = ConfusionMatrixDisplay(cm, display_labels=list(race_to_id.keys()))\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "cmp.plot(ax=ax, xticks_rotation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fd214bdf-6afb-4be7-b82f-0b808a8b47e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for class other: 0.15\n",
      "Accuracy for class nh_black: 0.17\n",
      "Accuracy for class nh_white: 0.73\n",
      "Accuracy for class hispanic: 0.66\n",
      "Accuracy for class asian: 0.05\n"
     ]
    }
   ],
   "source": [
    "for class_id in id_to_race:\n",
    "    correct = np.sum((y_test == class_id) & (preds == class_id))\n",
    "    total = np.sum(y_test == class_id)\n",
    "    print(f'Accuracy for class {id_to_race[class_id]}: {correct / total:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d4ffcdb-0dfa-4135-92f0-4ee9bc1cca04",
   "metadata": {
    "tags": []
   },
   "source": [
    "### For 2M Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "775560d2-5583-4faf-8451-75ab19d09b18",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done  30 tasks      | elapsed:    2.5s\n",
      "[Parallel(n_jobs=10)]: Done  98 out of 100 | elapsed:    5.7s remaining:    0.1s\n",
      "[Parallel(n_jobs=10)]: Done 100 out of 100 | elapsed:    5.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       asian       0.09      0.05      0.06      6867\n",
      "    hispanic       0.69      0.66      0.68     38961\n",
      "    nh_black       0.34      0.17      0.23     13726\n",
      "    nh_white       0.60      0.72      0.65     60970\n",
      "       other       0.17      0.16      0.17     14374\n",
      "\n",
      "    accuracy                           0.55    134898\n",
      "   macro avg       0.38      0.35      0.36    134898\n",
      "weighted avg       0.53      0.55      0.53    134898\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fc7604e1660>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_2m = joblib.load('models/rf_model_lastname_2M.joblib')\n",
    "preds = model_2m.predict(X_test)\n",
    "predicted = [id_to_race[p] for p in preds]\n",
    "actual = [id_to_race[p] for p in y_test.values]\n",
    "\n",
    "print(classification_report(actual, predicted))\n",
    "\n",
    "cm = confusion_matrix(actual, predicted, labels=list(race_to_id.keys()))\n",
    "cmp = ConfusionMatrixDisplay(cm, display_labels=list(race_to_id.keys()))\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "cmp.plot(ax=ax, xticks_rotation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "737f723f-5fd7-4465-b57e-6ace394085d5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for class other: 0.16\n",
      "Accuracy for class nh_black: 0.17\n",
      "Accuracy for class nh_white: 0.72\n",
      "Accuracy for class hispanic: 0.66\n",
      "Accuracy for class asian: 0.05\n"
     ]
    }
   ],
   "source": [
    "for class_id in id_to_race:\n",
    "    correct = np.sum((y_test == class_id) & (preds == class_id))\n",
    "    total = np.sum(y_test == class_id)\n",
    "    print(f'Accuracy for class {id_to_race[class_id]}: {correct / total:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a95f50f-487a-411b-b3e5-eaf6af672206",
   "metadata": {
    "tags": []
   },
   "source": [
    "### For Model trained  with complete dataset (20 iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7401ff7d-a24f-488b-b9f7-58ac0b45f5d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done   5 out of  20 | elapsed:    0.4s remaining:    1.3s\n",
      "[Parallel(n_jobs=10)]: Done   9 out of  20 | elapsed:    0.5s remaining:    0.6s\n",
      "[Parallel(n_jobs=10)]: Done  13 out of  20 | elapsed:    0.8s remaining:    0.4s\n",
      "[Parallel(n_jobs=10)]: Done  17 out of  20 | elapsed:    0.8s remaining:    0.1s\n",
      "[Parallel(n_jobs=10)]: Done  20 out of  20 | elapsed:    0.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       asian       0.09      0.05      0.06      6867\n",
      "    hispanic       0.68      0.66      0.67     38961\n",
      "    nh_black       0.33      0.19      0.24     13726\n",
      "    nh_white       0.61      0.70      0.65     60970\n",
      "       other       0.17      0.17      0.17     14374\n",
      "\n",
      "    accuracy                           0.55    134898\n",
      "   macro avg       0.37      0.35      0.36    134898\n",
      "weighted avg       0.53      0.55      0.53    134898\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fc6ecf53760>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_full = joblib.load('models/rf_model_lastname.joblib')\n",
    "preds = model_full.predict(X_test)\n",
    "predicted = [id_to_race[p] for p in preds]\n",
    "actual = [id_to_race[p] for p in y_test.values]\n",
    "\n",
    "print(classification_report(actual, predicted))\n",
    "\n",
    "cm = confusion_matrix(actual, predicted, labels=list(race_to_id.keys()))\n",
    "cmp = ConfusionMatrixDisplay(cm, display_labels=list(race_to_id.keys()))\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "cmp.plot(ax=ax, xticks_rotation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f393c515-acd7-4d27-8af5-4c2b9541e976",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for class other: 0.17\n",
      "Accuracy for class nh_black: 0.19\n",
      "Accuracy for class nh_white: 0.70\n",
      "Accuracy for class hispanic: 0.66\n",
      "Accuracy for class asian: 0.05\n"
     ]
    }
   ],
   "source": [
    "for class_id in id_to_race:\n",
    "    correct = np.sum((y_test == class_id) & (preds == class_id))\n",
    "    total = np.sum(y_test == class_id)\n",
    "    print(f'Accuracy for class {id_to_race[class_id]}: {correct / total:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "80bf6183-fda0-4b73-8952-82c03d044580",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "899M\tmodels/rf_model_fullname_1M.joblib\n",
      "1.8G\tmodels/rf_model_fullname_2M.joblib\n",
      "1.4G\tmodels/rf_model_fullname.joblib\n",
      "960M\tmodels/rf_model_lastname_1M.joblib\n",
      "1.1G\tmodels/rf_model_lastname_2M.joblib\n",
      "206M\tmodels/rf_model_lastname.joblib\n"
     ]
    }
   ],
   "source": [
    "!du -sh models/rf_model_*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6059a1bf-b61c-4dfe-910c-50d1b5e53d3d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cAxKJpEDqLGyiCD1HjhxB/fr1YWBgAGNjY7Rp0wZhYWEAgIyMDAwbNgwWFhbQ1NSEjY0NfH195du+f3hr/PjxcHJyQpkyZWBvb4+pU6ciMzNTvn7GjBmoWrUqNm/eDFtbW+jr66NHjx5ISVE+ZQ4A6enpSE5OVrgVp4Skl8iWyWDy3iETE0MdxMQr70dsfApMDHXeK6+L2HfKTxvWEQ7ly6Jhj1mo2HwcBk5cjWnDO8HTvULBd6KUSUxJQ7ZMlmtK2UhfB/F5HG7Jj4pO1pgyvAsWT/fBmCHt8exFAoZOWo2XIj/c8maMGysbswnKn3+xCSm5DiOaGOjIx7h9eTNYmhlg8dpDSEpJQ0ZmFtZs+wfPY5IQE1e8z+mSwKCMGlSl0lwzNfEvM2Cso650m0dxaZj99z2M2XYT0/begVQCrBvgCTPdnA+ib7aLe6/OuJcZMNZWXqdYJP33mmKo7DUlj0Pmn+rslXtIffkaLRtVL5D6ioIoTmR++fIlRo8eDXd3d6SmpmLatGno2LEjgoKCsHTpUuzfvx87duxA+fLlERkZicjIvA+36Orqws/PD5aWlrh9+zYGDx4MXV1djBs3Tl4mLCwM+/btw8GDB5GQkIBu3bph7ty5mDNnjtI6fX19MXPmzALvd0mzed9Z3Lz3CCtnD4BlWUNcvf0Qs5bugZmxHurV4GxPYahTw1n+t4OtBSo6WqPT/+bjn3O30baZRzG27OujpqqCpTN8MGXRDtTqOA0q/80cNazpAkEQirt5pdLtJ0m4/SRJfv9mZBJ2/lAHnTyssPLUw2JsGQGA/8lrqFXNESZGesXdlHwTRejp3Lmzwv3169fD1NQUd+/exePHj+Ho6Ij69etDIpHAxsbmg3VNmTJF/retrS3GjBmDbdu2KYQemUwGPz8/6OrmfCrs06cPTp48mWfomThxIkaPfns1TXJyMqytlZ9dXxQM9bWhIpUi9r0ZhtiEVJgaKT9h1sRIF7EJqe+VT4HJf+Vfp2di8brDWD7TB41quwEAXCpY4l7oU6zfGSD60GOgWwYqUmmuT2DxSakw+sBJyp9KV0cL1pYmePI8rsDqLI3ejPE4ZWPWUPkLuImhbq6TnGMTU+VjHAAqOZXDvlWjkZL6CplZ2TAy0EG3Yb+hUh5X4IlJYlomsmQyGL03A2OkrY641Iw8tlKULRMQHJWCcoZlAEC+nfF7dRhrqyPkxefPkH4N9P97TUlQ9pqSx0nKn+J5dAKu3w7D7LHffnFdRUkUh7cePHiAnj17wt7eHnp6erC1tQUAPH78GD4+PggKCoKzszOGDx+OY8eOfbCu7du3o169ejA3N4eOjg6mTJmCx48Vj4/a2trKAw8AWFhYIDo6Os86NTQ0oKenp3ArTupqqqjoVA4XAx/Il8lkMlwMfICqbspDYVU3G1y88UBh2YXrIajmZgsAyMrKRmZWNqTvHfdVkUohk/FTsJqaKpwrWOL6rVD5MplMhmu3wlDJOe9LqD9V2qt0PH0e/8GrvcQgZ4xbKYxZmUyGS4GhHx7jgbnHuLLyujpaMDLQQcSTGNwJeYLGdSvmKiM2WTIB95+lwNPeSL5MAsDT3gi3nyTmqw6pBHAoq4PY1JzDs08TXyE2JV2hTm11FVQsp4dbkUl5VSMKamqqcKpgieu3w+TLZDIZbtwKQ0WnL39NOXTqBgz0tBVmk0sDUYSetm3bIj4+HmvWrMHly5dx+fJlADnn81SvXh3h4eGYPXs2Xr16hW7duqFLly5K67l48SJ69eqFVq1a4eDBgwgMDMTkyZORkaH4KUVNTfHyS4lEAplMVjidKyT9uzTEDv/L2HP0KkIfvcD0Jbvx6nUGOjevCQAYO/cvLFzrLy/fr1MDnL16H+t2BCDs8Qss3XgUd0KeoHeHnO940NHWRM0qFTB/9UFcDgpFZFQc9hy5gn3Hr6FZ/crF0seSpkf7+th//BoO/XMDEZHRWLDyb7x+nYE2TXKOl89ashMrNh+Vl8/MzELIw2cIefgMWVnZiIlPRsjDZ3gS9XYWZ9mGQwi88xBRLxJw+/4jTJy7BSpSCZo1cC/y/pU0Pp29sPPQZew9dhVhj15gxm978Op1Bjq18AQAjJ+7FYvWHpKX79OpAc5dDcb6nQF4+DgayzYexb8hT9Cr/dvvMTly+mbO+H4Wh5Pn72DA+NVoUrcS6nuUrjeGwvLXpcfoUN0SratYwNakDCa0cYGWmgoOBOVc9j+jQ0X80OTtOX6DGtqhlr0RrAy04Gyui1kdK8FcXxN/33gmL7P18mMMaGCHhk4mqGCmjRkdKyI2JR2n78cUef9Kmm5t6+HgiWs4fOoGIp5EY9Hq/XiVnoFWjWsAAOYs3YlVfyq+pjwIf4YH4c+QmZWN2LhkPAhXfE0BcsLT4X9uoIV3daiq5H3laEn01R/eiouLQ3BwMNasWYMGDRoAAM6dO6dQRk9PD927d0f37t3RpUsXtGjRAvHx8TAyMlIod+HCBdjY2GDy5MnyZY8eKb/So7Rr3aga4pNeYqnfUcQkJMO1ghXWzR0sn8qPik5UmLWpXtEOiyb3xpL1h7F4/SHYWpni91n94WRnIS/z65TeWLT2EH76ZQuSUtJgWdYQowa0Qs+2dYq8fyVR0/ruSEx6iTVbTyA+IQWOdhZYPL0/jAxy9vmLGMV9HhufAp/Ry+X3/9p3Fn/tO4tqFe3w+5zBAIDouCRMX7QdSSlpMNDXhrurDVbPGwpD/S+f3i7tWjWqivikVCzzO4qYhBS4VrDEGt9B8hP4n0UnQCJ9d4zbYuGkXliy4Qh+XX8YtlYmWD7TR2GMR8cnY+7K/Yj771Bw+2YeGNq7aZH3raQ6/u8LGJRRw3fe9jDW0UDI8xQM3xIoP7nZXF9T4fwnXS01TG7rCmMdDaS8zsS9ZykYuP4awmNfystsOv8IWmoqmNTWFTqaqrj5OBHD/wxCRnbp+qBZGJrUy3lNWb/tJOITU+BgZ4GFU3zkh7dexCYpXHUVm5CCgWN+l9/ftv8ctu0/h6oV7bB01iD58mu3wvAiNhGtm9Qous4UEInwlZ9hJ5PJYGZmhpYtW2L69Ol4/PgxJkyYgKtXr2Lv3r14+PAhLCwsUK1aNUilUsyfPx/+/v54+vQppFIpJBIJ9u7diw4dOmD//v3o3LkzNm/eDE9PT/j7+2PmzJnIzs5GYmIigJyrt/bt24egoCB5G5YsWYIlS5YgIiIiX21OTk6Gvr4+/g2Phm4xH+oSi9gUcV/NVByM8rhihwpPmyXnPl6ICsz6/p7F3QTRSE1JRuOq5ZGUlPTBU0S++sNbUqkU27Ztw/Xr11GpUiWMGjUKCxYskK/X1dXF/Pnz4eHhAU9PT0RERODQoUOQSnPvmnbt2mHUqFEYNmwYqlatigsXLmDq1KlF2R0iIiL6TF/9TE9pxJmeoseZnqLHmZ6ix5meosWZnqLDmR4iIiKidzD0EBERkSgw9BAREZEoMPQQERGRKDD0EBERkSgw9BAREZEoMPQQERGRKDD0EBERkSgw9BAREZEoMPQQERGRKDD0EBERkSgw9BAREZEoMPQQERGRKDD0EBERkSgw9BAREZEoMPQQERGRKDD0EBERkSgw9BAREZEoMPQQERGRKDD0EBERkSgw9BAREZEoMPQQERGRKDD0EBERkSgw9BAREZEoMPQQERGRKDD0EBERkSgw9BAREZEoMPQQERGRKDD0EBERkSgw9BAREZEoMPQQERGRKDD0EBERkSgw9BAREZEoMPQQERGRKDD0EBERkSgw9BAREZEoMPQQERGRKDD0EBERkSioFncDKDdBEAAAqSkpxdwS8UhNTS/uJoiOmqBe3E0Qnez0l8XdBFFJTUku7iaIxsvUnPfLN++feZEIHytBRe7JkyewtrYu7mYQERGVKpGRkShXrlye6xl6SiCZTIZnz55BV1cXEomkuJuTb8nJybC2tkZkZCT09PSKuzlfPe7vosd9XrS4v4tead3ngiAgJSUFlpaWkErzPnOHh7dKIKlU+sGkWtLp6emVqidLacf9XfS4z4sW93fRK437XF9f/6NleCIzERERiQJDDxEREYkCQw8VGA0NDUyfPh0aGhrF3RRR4P4uetznRYv7u+h97fucJzITERGRKHCmh4iIiESBoYeIiIhEgaGHiIiIRIGhh76It7c3Ro4cWdzNKBUkEgn27dv3WdsGBARAIpEgMTExzzJ+fn4wMDD4rPo/9zFLsi/Z35/K1tYWS5YsyXN9REQEJBIJgoKCiqQ9he1jz/ui3Pf5xdeqjyuJ/7eCxi8npHwJCAhAo0aNkJCQUKBvrERiYG1tjaioKJiYmAD4+p9PUVFRMDQ0LO5mKNizZw/U1NSKuxklWkn8vxU0hh4qcTIyMqCuzh+jpK+HiooKzM3Ni7sZRaYk9tXIyKi4m1DilcT/W0Hj4S2SS09Px/Dhw2FmZgZNTU3Ur18fV69eRUREBBo1agQAMDQ0hEQigY+Pj3w7mUyGcePGwcjICObm5pgxY4ZCvYmJiRg0aBBMTU2hp6eHxo0b4+bNm/L1M2bMQNWqVbF27VrY2dlBU1OzKLpb4Ly9vTF8+PAP7ovY2Fh07NgRZcqUgaOjI/bv3/9Jj3H+/Hm4u7tDU1MTtWvXxp07d/IsGxYWhvbt26Ns2bLQ0dGBp6cnTpw4oVAmPT0d48ePh7W1NTQ0NODg4IB169YprS8tLQ0tW7ZEvXr1SsQhr8Lc3x4eHli4cKH8focOHaCmpobU1FQAOT8KLJFIEBoaKi+TlpaGAQMGQFdXF+XLl8fq1avl6949vPWh55NMJoOvry/s7OygpaWFKlWqYNeuXZ+zewrdh5737x4mycjIwLBhw2BhYQFNTU3Y2NjA19dXoeyKFSvQsmVLaGlpwd7ePlefx48fDycnJ5QpUwb29vaYOnUqMjMz5evfvIZs3rwZtra20NfXR48ePZCSkiIv8/7hrU8Z+6XJkSNHUL9+fRgYGMDY2Bht2rRBWFgYgPz9L949vFUQ+73EEYj+M3z4cMHS0lI4dOiQ8O+//wr9+vUTDA0NhdjYWGH37t0CACE4OFiIiooSEhMTBUEQBC8vL0FPT0+YMWOGEBISImzcuFGQSCTCsWPH5PU2bdpUaNu2rXD16lUhJCRE+OmnnwRjY2MhLi5OEARBmD59uqCtrS20aNFCuHHjhnDz5s1i6f+X+ti+ACCUK1dO+Ouvv4QHDx4Iw4cPF3R0dOT74UNOnTolABBcXV2FY8eOCbdu3RLatGkj2NraChkZGYIgCMKGDRsEfX19+TZBQUHCypUrhdu3bwshISHClClTBE1NTeHRo0fyMt26dROsra2FPXv2CGFhYcKJEyeEbdu2KTxmQkKCkJCQINStW1f45ptvhJcvXxbgXvt8hbm/R48eLbRu3VoQBEGQyWSCkZGRYGJiIhw+fFgQBEH4888/BSsrK3l5GxsbwcjISPj999+FBw8eCL6+voJUKhXu378vCIIghIeHCwCEwMBAISsrK8/n088//yy4uLgIR44cEcLCwoQNGzYIGhoaQkBAQIHuuy+Vn32/d+9eQRAEYcGCBYK1tbVw5swZISIiQjh79qzw119/yesCIBgbGwtr1qwRgoODhSlTpggqKirC3bt35WVmz54tnD9/XggPDxf2798vlC1bVpg3b558/fTp0wUdHR2hU6dOwu3bt4UzZ84I5ubmwqRJkxTaPGLECPn9D4390mzXrl3C7t27hQcPHgiBgYFC27ZthcqVKwvZ2dn5+l+8+b8JQsHs95KGoYcEQRCE1NRUQU1NTdiyZYt8WUZGhmBpaSnMnz9f4Q3wXV5eXkL9+vUVlnl6egrjx48XBEEQzp49K+jp6QmvX79WKFOhQgVh1apVgiDkPHHU1NSE6OjoQuhZ0fnYvgAgTJkyRb4uNTVVACB/I/2QN/v/3RfluLg4QUtLS9i+fbsgCLlDjzIVK1YUli1bJgiCIAQHBwsAhOPHj3/wMe/duye4u7sLnTt3FtLT0z/a1qJSmPt7//79gr6+vpCVlSUEBQUJ5ubmwogRI+R1Dxo0SPj222/l5W1sbITevXvL78tkMsHMzExYsWKFIAiKoUcQBKXPp9evXwtlypQRLly4oNCWgQMHCj179szHHik6+dn3b948f/zxR6Fx48aCTCZTWhcAYciQIQrLatWqJQwdOjTPx1+wYIFQo0YN+f3p06cLZcqUEZKTk+XLxo4dK9SqVUuhzW9Cz8fG/tckJiZGACDcvn07X/+Ld0PP+z5nv5c0PLxFAHIOhWRmZqJevXryZWpqaqhZsybu3bv3wW3d3d0V7ltYWCA6OhoAcPPmTaSmpsLY2Bg6OjryW3h4uHzKFQBsbGxgampagD0qHh/aF++v19bWhp6ensL6j6lTp478byMjIzg7O+f5/0lNTcWYMWPg6uoKAwMD6Ojo4N69e3j8+DEAICgoCCoqKvDy8vrgYzZr1gwODg7Yvn17iTvXqrD2d4MGDZCSkoLAwECcPn0aXl5e8Pb2RkBAAADg9OnT8Pb2zrMtEokE5ubmn/S/DQ0NRVpaGpo1a6bwXNm0aZPCc6Wk+Ni+f8PHxwdBQUFwdnbG8OHDcezYsVxl3h3Xb+6/O663b9+OevXqwdzcHDo6OpgyZYp8HL9ha2sLXV3dj7YHyP/YL40ePHiAnj17wt7eHnp6erC1tQUAPH78OF//i3cV9H4vCXgiM32x96+IkEgkkMlkAHLeeC0sLORvFu9696oVbW3twmxikfnQvsjP+oI0ZswYHD9+HAsXLoSDgwO0tLTQpUsXZGRkAAC0tLTyVU/r1q2xe/du3L17F5UrVy6Utn6uwtrfBgYGqFKlCgICAnDx4kU0a9YMDRs2RPfu3RESEoIHDx7kesP80v/tm/OF/P39YWVlpbCuJP4OUn77W716dYSHh+Pw4cM4ceIEunXrhqZNm+b7XKWLFy+iV69emDlzJpo3bw59fX1s27YNixYt+qz2APkf+6VR27ZtYWNjgzVr1sDS0hIymQyVKlVCRkbGJ/0vCmO/lwQMPQQAqFChAtTV1XH+/HnY2NgAADIzM3H16lWMHDlS/gk/Ozv7k+qtXr06nj9/DlVVVfknDvp8ly5dQvny5QEACQkJCAkJgaurq9Ky58+fh4+PDzp27Agg5001IiJCvr5y5cqQyWQ4ffo0mjZtmudjzp07Fzo6OmjSpAkCAgLg5uZWcB0qwby8vHDq1ClcuXIFc+bMgZGREVxdXTFnzhxYWFjAycnps+tW9nxyc3ODhoYGHj9+/NXNQOjp6aF79+7o3r07unTpghYtWiA+Pl5+RdWlS5fQt29feflLly6hWrVqAIALFy7AxsYGkydPlq9/9OjRF7Unv2O/tImLi0NwcDDWrFmDBg0aAADOnTunUOZj/4s3CmO/lwQMPQQgZ6Zl6NChGDt2LIyMjFC+fHnMnz8faWlpGDhwINLS0iCRSHDw4EG0atUKWlpa0NHR+Wi9TZs2RZ06ddChQwfMnz8fTk5OePbsGfz9/dGxY0d4eHgUQe++HrNmzYKxsTHKli2LyZMnw8TEBB06dFBa1tHREXv27EHbtm0hkUgwdepUhU9gtra26NevHwYMGIClS5eiSpUqePToEaKjo9GtWzeFuhYuXIjs7Gw0btwYAQEBcHFxKcxulgje3t5YtmwZTE1N5f319vbG8uXL0bVr1y+q28bGJtfzSVdXF2PGjMGoUaMgk8lQv359JCUl4fz589DT00O/fv0KoltFbvHixbCwsEC1atUglUqxc+dOmJubK8z07ty5Ex4eHqhfvz62bNmCK1euyK+kcnR0xOPHj7Ft2zZ4enrC398fe/fu/aI2fcrYL00MDQ1hbGyM1atXw8LCAo8fP8aECRPk6/Pzv3ijMPZ7ScBzekhu7ty56Ny5M/r06YPq1asjNDQUR48ehaGhIaysrDBz5kxMmDABZcuWxbBhw/JVp0QiwaFDh9CwYUP0798fTk5O6NGjBx49eoSyZcsWco++PnPnzsWIESNQo0YNPH/+HAcOHMjzPJvFixfD0NAQdevWRdu2bdG8eXNUr15docyKFSvQpUsXfP/993BxccHgwYPx8uVLpfX9+uuv6NatGxo3boyQkJAC71tJ06BBA8hkMoVZF29vb2RnZ+c6n+dT5fV8mj17NqZOnQpfX1+4urqiRYsW8Pf3h52d3Rc9XnHS1dXF/Pnz4eHhAU9PT0RERODQoUOQSt++/cycORPbtm2Du7s7Nm3ahK1bt8pnFNu1a4dRo0Zh2LBhqFq1Ki5cuICpU6d+cbs+ZeyXFlKpFNu2bcP169dRqVIljBo1CgsWLJCvz8//4o3C2u/FTSIIglDcjSAiInGSSCTYu3dvnjOWRAWJMz1EREQkCgw9RCXAkCFDFC5Tfvc2ZMiQ4m7eV4f7m0iceHiLqASIjo5GcnKy0nV6enowMzMr4hZ93bi/icSJoYeIiIhEgYe3iIiISBQYeoiIiEgUGHqIiIhIFBh6iIiISBQYeojoq+Lj46PwRXfe3t4YOXJkkbcjICAAEokEiYmJeZaRSCTYt29fvuucMWMGqlat+kXtioiIgEQiQVBQ0BfVQ1QaMfQQUaHz8fGBRCKBRCKBuro6HBwcMGvWLGRlZRX6Y+/ZswezZ8/OV9n8BBUiKr34g6NEVCRatGiBDRs2ID09HYcOHcIPP/wANTU1TJw4MVfZjIyMPH9T7FO9/+vRRCRenOkhoiKhoaEBc3Nz2NjYYOjQoWjatCn2798P4O0hqTlz5sDS0hLOzs4AgMjISHTr1g0GBgYwMjJC+/btERERIa8zOzsbo0ePhoGBAYyNjTFu3Di8/9Vj7x/eSk9Px/jx42FtbQ0NDQ04ODhg3bp1iIiIQKNGjQDk/Fq1RCKBj48PAEAmk8HX1xd2dnbQ0tJClSpVsGvXLoXHOXToEJycnKClpYVGjRoptDO/xo8fDycnJ5QpUwb29vaYOnUqMjMzc5VbtWoVrK2tUaZMGXTr1g1JSUkK69euXQtXV1doamrCxcUFf/zxxye3hehrxNBDRMVCS0sLGRkZ8vsnT55EcHAwjh8/joMHDyIzMxPNmzeHrq4uzp49i/Pnz0NHRwctWrSQb7do0SL4+flh/fr1OHfuHOLj47F3794PPm7fvn2xdetWLF26FPfu3cOqVaugo6MDa2tr7N69GwAQHByMqKgo/PbbbwAAX19fbNq0CStXrsS///6LUaNGoXfv3jh9+jSAnHDWqVMntG3bFkFBQRg0aBAmTJjwyftEV1cXfn5+uHv3Ln777TesWbMGv/76q0KZ0NBQ7NixAwcOHMCRI0cQGBiI77//Xr5+y5YtmDZtGubMmYN79+7hl19+wdSpU7Fx48ZPbg/RV0cgIipk/fr1E9q3by8IgiDIZDLh+PHjgoaGhjBmzBj5+rJlywrp6enybTZv3iw4OzsLMplMviw9PV3Q0tISjh49KgiCIFhYWAjz58+Xr8/MzBTKlSsnfyxBEAQvLy9hxIgRgiAIQnBwsABAOH78uNJ2njp1SgAgJCQkyJe9fv1aKFOmjHDhwgWFsgMHDhR69uwpCIIgTJw4UXBzc1NYP378+Fx1vQ+AsHfv3jzXL1iwQKhRo4b8/vTp0wUVFRXhyZMn8mWHDx8WpFKpEBUVJQiCIFSoUEH466+/FOqZPXu2UKdOHUEQBCE8PFwAIAQGBub5uERfK57TQ0RF4uDBg9DR0UFmZiZkMhm+/fZbzJgxQ76+cuXKCufx3Lx5E6GhodDV1VWo5/Xr1wgLC0NSUhKioqJQq1Yt+TpVVVV4eHjkOsT1RlBQEFRUVODl5ZXvdoeGhiItLQ3NmjVTWJ6RkYFq1aoBAO7du6fQDgCoU6dOvh/jje3bt2Pp0qUICwtDamoqsrKyoKenp1CmfPnysLKyUngcmUyG4OBg6OrqIiwsDAMHDsTgwYPlZbKysqCvr//J7SH62jD0EFGRaNSoEVasWAF1dXVYWlpCVVXx5UdbW1vhfmpqKmrUqIEtW7bkqsvU1PSz2qClpfXJ26SmpgIA/P39FcIGkHOeUkG5ePEievXqhZkzZ6J58+bQ19fHtm3bsGjRok9u65o1a3KFMBUVlQJrK1FpxdBDREVCW1sbDg4O+S5fvXp1bN++HWZmZrlmO96wsLDA5cuX0bBhQwA5MxrXr19H9erVlZavXLkyZDIZTp8+jaZNm+Za/2amKTs7W77Mzc0NGhoaePz4cZ4zRK6urvKTst+4dOnSxzv5jgsXLsDGxgaTJ0+WL3v06FGuco8fP8azZ89gaWkpfxypVApnZ2eULVsWlv9v595BGonCAAqfdCKkExQFH6BgCh+tlZ2IhWIQmyADPkBCiAQVbFIEwVhZxEILIbEREYQptBcDloLY+CAKYmerYLdbLASW3RXSyc756uHOvd3h8s+0t/P09EQqlWro/VIUOMgs6VtKpVK0tLQwNTVFtVrl+fmZi4sLstksr6+vAKysrLC9vU0Yhtzd3ZFOp7/8x053dzdBEDA/P08YhvU1T05OAOjq6iIWi3F2dsbb2xvv7+/E43HW1tbI5XIcHh5Sq9W4vr5md3e3Phy8vLzM4+Mj6+vr3N/fc3R0RKVSaei8fX19vLy8cHx8TK1Wo1Qq/XUou6mpiSAIuLm5oVqtks1mmZ2dpa2tDYBCoUCxWKRUKvHw8MDt7S3lcpmdnZ2G9iP9j4weSd9Sc3Mzl5eXdHZ2kkwmSSQSLCws8Pn5Wb/5WV1dZW5ujiAIGBkZIR6PMz09/eW6e3t7zMzMkE6n6e/vZ2lpiY+PDwA6OjooFApsbGzQ2tpKJpMBYHNzk3w+T7FYJJFIMD4+zvn5OT09PcCvOZvT01PCMGRoaIj9/X22trYaOu/k5CS5XI5MJsPw8DBXV1fk8/k/nuvt7SWZTDIxMcHY2BiDg4O/fZK+uLjIwcEB5XKZgYEBRkdHqVQq9b1KURb78a+JP0mSpP+INz2SJCkSjB5JkhQJRo8kSYoEo0eSJEWC0SNJkiLB6JEkSZFg9EiSpEgweiRJUiQYPZIkKRKMHkmSFAlGjyRJioSf4bMqLPTos5gAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
    "\n",
    "def plot_confusion_matrix(y_preds, y_true, labels):\n",
    "    cm = confusion_matrix(y_true, y_preds, normalize=\"true\")\n",
    "    fig, ax = plt.subplots(figsize=(6, 6))\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
    "    disp.plot(cmap=\"Blues\", values_format=\".2f\", ax=ax, colorbar=False)\n",
    "    plt.title(\"Normalized confusion matrix\")\n",
    "    plt.show()\n",
    "\n",
    "#y_preds = lr_clf.predict(X_valid)\n",
    "plot_confusion_matrix(predicted, actual, list(race_to_id.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1f562b9-3aaa-47c7-80e4-cfd63b297d43",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.6"
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 "nbformat": 4,
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}