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yann22ahlgrim
2023-07-15 14:34:17 +02:00
parent 0441af8000
commit 68f7b7be97
1 changed files with 20 additions and 40 deletions
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competitions/identify-age-related-conditions/train_models/RF_model.ipynb
+20 -40
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@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 69,
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
@@ -11,14 +11,17 @@
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.model_selection import train_test_split, cross_val_score\n",
"from sklearn.metrics import accuracy_score\n",
"import pandas as pd"
"from xgboost import XGBClassifier\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 70,
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
@@ -32,7 +35,7 @@
},
{
"cell_type": "code",
"execution_count": 71,
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
@@ -44,7 +47,7 @@
},
{
"cell_type": "code",
"execution_count": 72,
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
@@ -71,7 +74,7 @@
},
{
"cell_type": "code",
"execution_count": 73,
"execution_count": 69,
"metadata": {},
"outputs": [
{
@@ -85,7 +88,8 @@
],
"source": [
"# Define model\n",
"model = RandomForestClassifier(n_estimators=100, random_state=22)\n",
"#model = RandomForestClassifier(n_estimators=100, random_state=22)\n",
"model = XGBClassifier(n_estimators=500)\n",
"\n",
"#Splitting\n",
"train, greeks, test = load_dataset()\n",
@@ -102,47 +106,23 @@
},
{
"cell_type": "code",
"execution_count": 74,
"execution_count": 70,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "A given column is not a column of the dataframe",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"File \u001b[1;32mc:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3801\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 3802\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[0;32m 3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
"File \u001b[1;32mc:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mc:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 'Class'",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"File \u001b[1;32mc:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\utils\\__init__.py:448\u001b[0m, in \u001b[0;36m_get_column_indices\u001b[1;34m(X, key)\u001b[0m\n\u001b[0;32m 447\u001b[0m \u001b[39mfor\u001b[39;00m col \u001b[39min\u001b[39;00m columns:\n\u001b[1;32m--> 448\u001b[0m col_idx \u001b[39m=\u001b[39m all_columns\u001b[39m.\u001b[39;49mget_loc(col)\n\u001b[0;32m 449\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(col_idx, numbers\u001b[39m.\u001b[39mIntegral):\n",
"File \u001b[1;32mc:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m-> 3804\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[0;32m 3805\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[0;32m 3806\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3807\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3808\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n",
"\u001b[1;31mKeyError\u001b[0m: 'Class'",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[74], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m pipeline\u001b[39m.\u001b[39;49mfit(X_train, y_train)\n\u001b[0;32m 2\u001b[0m preds \u001b[39m=\u001b[39m pipeline\u001b[39m.\u001b[39mpredict(X_valid)\n\u001b[0;32m 3\u001b[0m score \u001b[39m=\u001b[39m accuracy_score(y_valid, preds)\n",
"File \u001b[1;32mc:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\pipeline.py:401\u001b[0m, in \u001b[0;36mPipeline.fit\u001b[1;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[0;32m 375\u001b[0m \u001b[39m\"\"\"Fit the model.\u001b[39;00m\n\u001b[0;32m 376\u001b[0m \n\u001b[0;32m 377\u001b[0m \u001b[39mFit all the transformers one after the other and transform the\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 398\u001b[0m \u001b[39m Pipeline with fitted steps.\u001b[39;00m\n\u001b[0;32m 399\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 400\u001b[0m fit_params_steps \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_fit_params(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfit_params)\n\u001b[1;32m--> 401\u001b[0m Xt \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fit(X, y, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfit_params_steps)\n\u001b[0;32m 402\u001b[0m \u001b[39mwith\u001b[39;00m _print_elapsed_time(\u001b[39m\"\u001b[39m\u001b[39mPipeline\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_log_message(\u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msteps) \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m)):\n\u001b[0;32m 403\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_final_estimator \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mpassthrough\u001b[39m\u001b[39m\"\u001b[39m:\n",
"File \u001b[1;32mc:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\pipeline.py:359\u001b[0m, in \u001b[0;36mPipeline._fit\u001b[1;34m(self, X, y, **fit_params_steps)\u001b[0m\n\u001b[0;32m 357\u001b[0m cloned_transformer \u001b[39m=\u001b[39m clone(transformer)\n\u001b[0;32m 358\u001b[0m \u001b[39m# Fit or load from cache the current transformer\u001b[39;00m\n\u001b[1;32m--> 359\u001b[0m X, fitted_transformer \u001b[39m=\u001b[39m fit_transform_one_cached(\n\u001b[0;32m 360\u001b[0m cloned_transformer,\n\u001b[0;32m 361\u001b[0m X,\n\u001b[0;32m 362\u001b[0m y,\n\u001b[0;32m 363\u001b[0m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m 364\u001b[0m message_clsname\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mPipeline\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m 365\u001b[0m message\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_log_message(step_idx),\n\u001b[0;32m 366\u001b[0m \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfit_params_steps[name],\n\u001b[0;32m 367\u001b[0m )\n\u001b[0;32m 368\u001b[0m \u001b[39m# Replace the transformer of the step with the fitted\u001b[39;00m\n\u001b[0;32m 369\u001b[0m \u001b[39m# transformer. This is necessary when loading the transformer\u001b[39;00m\n\u001b[0;32m 370\u001b[0m \u001b[39m# from the cache.\u001b[39;00m\n\u001b[0;32m 371\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msteps[step_idx] \u001b[39m=\u001b[39m (name, fitted_transformer)\n",
"File \u001b[1;32mc:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\joblib\\memory.py:349\u001b[0m, in \u001b[0;36mNotMemorizedFunc.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 348\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m--> 349\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfunc(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
"File \u001b[1;32mc:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\pipeline.py:893\u001b[0m, in \u001b[0;36m_fit_transform_one\u001b[1;34m(transformer, X, y, weight, message_clsname, message, **fit_params)\u001b[0m\n\u001b[0;32m 891\u001b[0m \u001b[39mwith\u001b[39;00m _print_elapsed_time(message_clsname, message):\n\u001b[0;32m 892\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(transformer, \u001b[39m\"\u001b[39m\u001b[39mfit_transform\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m--> 893\u001b[0m res \u001b[39m=\u001b[39m transformer\u001b[39m.\u001b[39mfit_transform(X, y, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfit_params)\n\u001b[0;32m 894\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 895\u001b[0m res \u001b[39m=\u001b[39m transformer\u001b[39m.\u001b[39mfit(X, y, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfit_params)\u001b[39m.\u001b[39mtransform(X)\n",
"File \u001b[1;32mc:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\utils\\_set_output.py:142\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[1;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[39m@wraps\u001b[39m(f)\n\u001b[0;32m 141\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mwrapped\u001b[39m(\u001b[39mself\u001b[39m, X, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m--> 142\u001b[0m data_to_wrap \u001b[39m=\u001b[39m f(\u001b[39mself\u001b[39m, X, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 143\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(data_to_wrap, \u001b[39mtuple\u001b[39m):\n\u001b[0;32m 144\u001b[0m \u001b[39m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[0;32m 145\u001b[0m \u001b[39mreturn\u001b[39;00m (\n\u001b[0;32m 146\u001b[0m _wrap_data_with_container(method, data_to_wrap[\u001b[39m0\u001b[39m], X, \u001b[39mself\u001b[39m),\n\u001b[0;32m 147\u001b[0m \u001b[39m*\u001b[39mdata_to_wrap[\u001b[39m1\u001b[39m:],\n\u001b[0;32m 148\u001b[0m )\n",
"File \u001b[1;32mc:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\compose\\_column_transformer.py:724\u001b[0m, in \u001b[0;36mColumnTransformer.fit_transform\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m 722\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_n_features(X, reset\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m 723\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_validate_transformers()\n\u001b[1;32m--> 724\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_column_callables(X)\n\u001b[0;32m 725\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_validate_remainder(X)\n\u001b[0;32m 727\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fit_transform(X, y, _fit_transform_one)\n",
"File \u001b[1;32mc:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\compose\\_column_transformer.py:426\u001b[0m, in \u001b[0;36mColumnTransformer._validate_column_callables\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 424\u001b[0m columns \u001b[39m=\u001b[39m columns(X)\n\u001b[0;32m 425\u001b[0m all_columns\u001b[39m.\u001b[39mappend(columns)\n\u001b[1;32m--> 426\u001b[0m transformer_to_input_indices[name] \u001b[39m=\u001b[39m _get_column_indices(X, columns)\n\u001b[0;32m 428\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_columns \u001b[39m=\u001b[39m all_columns\n\u001b[0;32m 429\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_transformer_to_input_indices \u001b[39m=\u001b[39m transformer_to_input_indices\n",
"File \u001b[1;32mc:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\utils\\__init__.py:456\u001b[0m, in \u001b[0;36m_get_column_indices\u001b[1;34m(X, key)\u001b[0m\n\u001b[0;32m 453\u001b[0m column_indices\u001b[39m.\u001b[39mappend(col_idx)\n\u001b[0;32m 455\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m--> 456\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mA given column is not a column of the dataframe\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n\u001b[0;32m 458\u001b[0m \u001b[39mreturn\u001b[39;00m column_indices\n\u001b[0;32m 459\u001b[0m \u001b[39melse\u001b[39;00m:\n",
"\u001b[1;31mValueError\u001b[0m: A given column is not a column of the dataframe"
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of [0.94736842 0.89189189 0.91891892 0.97297297 0.89189189]\n"
]
}
],
"source": [
"#Fit the Model and make preds\n",
"pipeline.fit(X_train, y_train)\n",
"preds = pipeline.predict(X_valid)\n",
"score = accuracy_score(y_valid, preds)\n",
"print(f\"{score} out of {len(y_valid)} are right\")"
"score = cross_val_score(pipeline, X_valid, y_valid, cv=5, scoring='accuracy')\n",
"print(f\"Accuracy of {score}\")"
]
}
],