Compare commits
2
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Petrus
|
d3f6732211 | ||
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Petrus
|
53d5116bbd |
No files matched your search
+23
-25
@@ -6,12 +6,12 @@
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"metadata": {},
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"metadata": {},
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"source": [
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"source": [
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"### Creates a correlation matrix\n",
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"### Creates a correlation matrix\n",
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"We should probably remove or combine some features"
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"This used to see if we should remove or combine some features"
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]
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]
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 6,
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"id": "557ed2b5",
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"id": "557ed2b5",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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@@ -30,27 +30,25 @@
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"output_type": "stream",
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"output_type": "stream",
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"text": [
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 32561 entries, 0 to 32560\n",
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"Index: 30162 entries, 1 to 32560\n",
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"Data columns (total 15 columns):\n",
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"Data columns (total 13 columns):\n",
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" # Column Non-Null Count Dtype \n",
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" # Column Non-Null Count Dtype\n",
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"--- ------ -------------- ----- \n",
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"--- ------ -------------- -----\n",
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" 0 age 32561 non-null int64 \n",
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" 0 age 30162 non-null int64\n",
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" 1 workclass 32561 non-null object\n",
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" 1 workclass 30162 non-null int64\n",
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" 2 fnlwgt 32561 non-null int64 \n",
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" 2 education.num 30162 non-null int64\n",
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" 3 education 32561 non-null object\n",
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" 3 marital.status 30162 non-null int64\n",
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" 4 education.num 32561 non-null int64 \n",
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" 4 occupation 30162 non-null int64\n",
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" 5 marital.status 32561 non-null object\n",
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" 5 relationship 30162 non-null int64\n",
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" 6 occupation 32561 non-null object\n",
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" 6 race 30162 non-null int64\n",
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" 7 relationship 32561 non-null object\n",
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" 7 sex 30162 non-null int64\n",
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" 8 race 32561 non-null object\n",
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" 8 capital.gain 30162 non-null int64\n",
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" 9 sex 32561 non-null object\n",
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" 9 capital.loss 30162 non-null int64\n",
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" 10 capital.gain 32561 non-null int64 \n",
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" 10 hours.per.week 30162 non-null int64\n",
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" 11 capital.loss 32561 non-null int64 \n",
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" 11 native.country 30162 non-null int64\n",
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" 12 hours.per.week 32561 non-null int64 \n",
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" 12 income 30162 non-null int64\n",
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" 13 native.country 32561 non-null object\n",
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"dtypes: int64(13)\n",
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" 14 income 32561 non-null object\n",
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"memory usage: 3.2 MB\n"
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"dtypes: int64(6), object(9)\n",
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"memory usage: 3.7+ MB\n"
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]
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]
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}
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}
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],
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],
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@@ -91,8 +89,8 @@
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"plt.show()\n",
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"plt.show()\n",
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"\n",
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"\n",
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"#df_encoded.head(10)\n",
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"#df_encoded.head(10)\n",
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"df.info()\n",
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"df_encoded.info()\n",
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"\n"
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"#df.head(20)\n"
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]
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]
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},
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},
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{
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{
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+272
-26
@@ -47,24 +47,25 @@
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"weighted avg 0.81 0.81 0.81 6033\n",
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"weighted avg 0.81 0.81 0.81 6033\n",
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"\n",
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"\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"Index: 18096 entries, 13586 to 5836\n",
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"Index: 30162 entries, 1 to 32560\n",
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"Data columns (total 12 columns):\n",
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"Data columns (total 13 columns):\n",
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||||||
" # Column Non-Null Count Dtype\n",
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" # Column Non-Null Count Dtype\n",
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"--- ------ -------------- -----\n",
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"--- ------ -------------- -----\n",
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" 0 age 18096 non-null int64\n",
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" 0 age 30162 non-null int64\n",
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" 1 workclass 18096 non-null int64\n",
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" 1 workclass 30162 non-null int64\n",
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" 2 education.num 18096 non-null int64\n",
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" 2 education.num 30162 non-null int64\n",
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" 3 marital.status 18096 non-null int64\n",
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" 3 marital.status 30162 non-null int64\n",
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" 4 occupation 18096 non-null int64\n",
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" 4 occupation 30162 non-null int64\n",
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" 5 relationship 18096 non-null int64\n",
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" 5 relationship 30162 non-null int64\n",
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" 6 race 18096 non-null int64\n",
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" 6 race 30162 non-null int64\n",
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" 7 sex 18096 non-null int64\n",
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" 7 sex 30162 non-null int64\n",
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" 8 capital.gain 18096 non-null int64\n",
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" 8 capital.gain 30162 non-null int64\n",
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" 9 capital.loss 18096 non-null int64\n",
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" 9 capital.loss 30162 non-null int64\n",
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" 10 hours.per.week 18096 non-null int64\n",
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" 10 hours.per.week 30162 non-null int64\n",
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" 11 native.country 18096 non-null int64\n",
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" 11 native.country 30162 non-null int64\n",
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"dtypes: int64(12)\n",
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" 12 income 30162 non-null int64\n",
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"memory usage: 1.8 MB\n"
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"dtypes: int64(13)\n",
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"memory usage: 3.2 MB\n"
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]
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]
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}
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}
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],
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],
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@@ -72,7 +73,7 @@
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"import pandas as pd\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
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"from sklearn.model_selection import train_test_split, RandomizedSearchCV, KFold, StratifiedKFold, GridSearchCV\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
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"from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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@@ -81,6 +82,7 @@
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"from scipy.stats import randint\n",
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"from scipy.stats import randint\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"# Load dataset\n",
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"# Load dataset\n",
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"df = pd.read_csv('./Datasets/adult.csv', comment = '#')\n",
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"df = pd.read_csv('./Datasets/adult.csv', comment = '#')\n",
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"\n",
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"\n",
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@@ -146,7 +148,15 @@
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"print(\"Classification Report:\")\n",
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"print(\"Classification Report:\")\n",
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"print(classification_report(y_val, y_pred, target_names=[\"Poor\", \"Rich\"]))\n",
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"print(classification_report(y_val, y_pred, target_names=[\"Poor\", \"Rich\"]))\n",
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"\n",
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"\n",
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"X_train.info()"
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"df_encoded.info()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bfaae28c",
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"metadata": {},
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"source": [
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"### Hyperparameter tuning"
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]
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]
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},
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},
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{
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{
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@@ -162,13 +172,15 @@
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"Classification Report:\n",
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"Classification Report:\n",
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" precision recall f1-score support\n",
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" precision recall f1-score support\n",
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"\n",
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"\n",
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" Poor 0.89 0.92 0.90 4524\n",
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" Poor 0.88 0.93 0.90 4524\n",
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" Rich 0.73 0.65 0.68 1509\n",
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" Rich 0.75 0.62 0.68 1509\n",
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"\n",
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"\n",
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" accuracy 0.85 6033\n",
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" accuracy 0.85 6033\n",
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" macro avg 0.81 0.78 0.79 6033\n",
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" macro avg 0.81 0.78 0.79 6033\n",
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"weighted avg 0.85 0.85 0.85 6033\n",
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"weighted avg 0.85 0.85 0.85 6033\n",
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"\n"
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"\n",
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"Best max_depth: 12\n",
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"Best min_samples_split: 18\n"
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]
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]
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}
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}
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],
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],
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@@ -176,36 +188,270 @@
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"# Hyperparameters search space\n",
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"# Hyperparameters search space\n",
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"param_dist = {\n",
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"param_dist = {\n",
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" 'full_dt_classifier__max_depth': randint(3, 20),\n",
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" 'full_dt_classifier__max_depth': randint(3, 20),\n",
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" 'full_dt_classifier__min_samples_split': randint(2, 10),\n",
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" 'full_dt_classifier__min_samples_split': randint(2, 20),\n",
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" 'full_dt_classifier__min_samples_leaf': randint(1, 10),\n",
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" 'full_dt_classifier__min_samples_leaf': randint(1, 15),\n",
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"}\n",
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"}\n",
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"\n",
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"\n",
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"# Ranodmized search for hyperparameter tuning\n",
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"# Ranodmized search for hyperparameter tuning\n",
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"random_search = RandomizedSearchCV(\n",
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"random_search = RandomizedSearchCV(\n",
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" estimator=model,\n",
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" estimator=model,\n",
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" param_distributions=param_dist,\n",
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" param_distributions=param_dist,\n",
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" n_iter = 50,\n",
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" n_iter = 300,\n",
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" cv = 10,\n",
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" cv = 10,\n",
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" scoring = 'r2',\n",
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" scoring = 'r2',\n",
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" n_jobs = -1,\n",
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" n_jobs = -1,\n",
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" random_state = 42\n",
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" random_state = 42\n",
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")\n",
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")\n",
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"\n",
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"\n",
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"#K_fold = KFold(n_splits=10, shuffle=True, random_state=42)\n",
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"#cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
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"#grid_search = GridSearchCV(model, param_grid, scoring='accuracy', cv=cv, n_jobs=-1)\n",
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"\n",
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"# Fit search\n",
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"# Fit search\n",
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"random_search.fit(X_train, y_train)\n",
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"random_search.fit(X_train, y_train)\n",
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"random_search.fit(X_train, y_train)\n",
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"\n",
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"\n",
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"# Best model training\n",
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"# Best model training\n",
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"best_model = random_search.best_estimator_\n",
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"best_model = random_search.best_estimator_\n",
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"y_pred_best = best_model.predict(X_val)\n",
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"y_pred_best = best_model.predict(X_val)\n",
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"\n",
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"\n",
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"print(\"Classification Report:\")\n",
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"print(\"Classification Report:\")\n",
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"print(classification_report(y_val, y_pred_best, target_names=[\"Poor\", \"Rich\"]))"
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"print(classification_report(y_val, y_pred_best, target_names=[\"Poor\", \"Rich\"]))\n",
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"\n",
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"best_max_depth = best_model.named_steps['full_dt_classifier'].max_depth\n",
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"best_min_samples_split = best_model.named_steps['full_dt_classifier'].min_samples_split\n",
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"best_min_samples_leaf = best_model.named_steps['full_dt_classifier'].min_samples_leaf\n",
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"\n",
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"print(f'Best max_depth: {best_max_depth}')\n",
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"print(f'Best min_samples_split: {best_min_samples_split}')\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a37f45b4",
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"metadata": {},
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"source": [
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"### Classifier comparison\n",
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"(This is taken from the ensemble lab)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e68b1ea9",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import GridSearchCV, StratifiedKFold\n",
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"import time\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.metrics import (accuracy_score, precision_score, \n",
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" recall_score, f1_score, \n",
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" confusion_matrix, ConfusionMatrixDisplay)\n",
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"from sklearn.ensemble import (RandomForestClassifier, BaggingClassifier, \n",
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" AdaBoostClassifier, GradientBoostingClassifier)\n",
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"from xgboost import XGBClassifier # Requires installation of the package ; Not a native function in sklearn\n",
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"from lightgbm import LGBMClassifier # Requires installation of the package; Not a native function in sklearn\n",
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"from catboost import CatBoostClassifier # Requires installation of the package; Not a native function in sklearn\n",
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"\n",
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"class ClassifierComparisonOpt:\n",
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" def __init__(self, X, y, test_size=0.3, use_bootstrap=True, random_state=42, cv_folds=10):\n",
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" # Split data stratified by labels\n",
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" X_train, X_test, y_train, y_test = train_test_split(\n",
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" X, y, test_size=test_size, stratify=y, random_state=random_state)\n",
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"\n",
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" # Scale features\n",
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" scaler = StandardScaler()\n",
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" self.X_train = scaler.fit_transform(X_train)\n",
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" self.X_test = scaler.transform(X_test)\n",
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"\n",
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" self.y_train = y_train\n",
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" self.y_test = y_test\n",
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" self.use_bootstrap = use_bootstrap\n",
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" self.cv_folds = cv_folds\n",
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" self.models = {}\n",
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" self.results = {}\n",
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" self.results_df = None\n",
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"\n",
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|
" # Define the classifiers and their hyperparameters\n",
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|
" def get_models_with_params(self):\n",
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|
" return {\n",
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||||||
|
" 'Decision Tree': (DecisionTreeClassifier(random_state=42), {\n",
|
||||||
|
" 'max_depth': [None, 10, 11, 12, 13, 14, 15, 16],\n",
|
||||||
|
" 'min_samples_split': [3, 4, 5, 6, 7, 8, 9]\n",
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" }),\n",
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|
" 'Random Forest': (RandomForestClassifier(bootstrap=self.use_bootstrap, random_state=42), {\n",
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|
" 'n_estimators': [50, 100, 200], # I currently don't know how to tune this\n",
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||||||
|
" 'max_depth': [None, 10, 11, 12, 13, 14, 15, 16],\n",
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|
" 'min_samples_split': [3, 4, 5, 6, 7, 8, 9]\n",
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" }),\n",
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|
" #'Bagging': (BaggingClassifier(bootstrap=self.use_bootstrap, random_state=42), {\n",
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|
" # 'n_estimators': [50, 100, 200],\n",
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||||||
|
" # 'max_samples': [0.5, 1.0],\n",
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||||||
|
" # 'oob_score': [True, False],\n",
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|
" #}),\n",
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|
" # SAMME: Stagewise Additive Modeling using a Multi-class Exponential loss\n",
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|
" #'AdaBoost': (AdaBoostClassifier(algorithm='SAMME', random_state=42), {\n",
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||||||
|
" # 'n_estimators': [50, 100, 200],\n",
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||||||
|
" # 'learning_rate': [0.01, 0.1, 0.2, 0.5, 1.0]\n",
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" #}),\n",
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" #'Gradient Boosting': (GradientBoostingClassifier(random_state=42), {\n",
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" # 'n_estimators': [50, 100, 200],\n",
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||||||
|
" # 'learning_rate': [0.01, 0.1, 0.2, 0.5, 1.0],\n",
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" # 'max_depth': [3, 5, 10]\n",
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||||||
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" #}),\n",
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" #'XGBoost': (XGBClassifier(eval_metric='mlogloss', random_state=42), {\n",
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" # 'n_estimators': [50, 100, 200],\n",
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||||||
|
" # 'learning_rate': [0.01, 0.1, 0.2, 0.5, 1.0],\n",
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||||||
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" # 'max_depth': [3, 5, 10]\n",
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||||||
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" #}),\n",
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" #'LightGBM': (LGBMClassifier(random_state=42), {\n",
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|
" # 'n_estimators': [50, 100, 200],\n",
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||||||
|
" # 'learning_rate': [0.01, 0.1, 0.2, 0.5, 1.0],\n",
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" # 'max_depth': [-1, 5, 10]\n",
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" #}),\n",
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" #'CatBoost': (CatBoostClassifier(verbose=0, random_state=42), {\n",
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||||||
|
" # 'iterations': [50, 100, 200],\n",
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||||||
|
" # 'learning_rate': [0.01, 0.1, 0.2, 0.5, 1.0],\n",
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" # 'depth': [4, 6, 10]\n",
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" #})\n",
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" }\n",
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"\n",
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||||||
|
" def fit_models(self):\n",
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|
" results_list = []\n",
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|
" cv = StratifiedKFold(n_splits=self.cv_folds, shuffle=True, random_state=42)\n",
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||||||
|
" models_with_params = self.get_models_with_params()\n",
|
||||||
|
"\n",
|
||||||
|
" for name, (model, param_grid) in models_with_params.items():\n",
|
||||||
|
" print(f\"Tuning {name} ...\")\n",
|
||||||
|
" \n",
|
||||||
|
" grid_search = GridSearchCV(model, param_grid, scoring='accuracy', cv=cv, n_jobs=-1)\n",
|
||||||
|
" \n",
|
||||||
|
" start_train = time.time()\n",
|
||||||
|
" grid_search.fit(self.X_train, self.y_train)\n",
|
||||||
|
" end_train = time.time()\n",
|
||||||
|
"\n",
|
||||||
|
" best_model = grid_search.best_estimator_\n",
|
||||||
|
" y_pred = best_model.predict(self.X_test)\n",
|
||||||
|
" end_pred = time.time()\n",
|
||||||
|
"\n",
|
||||||
|
" self.models[name] = {\n",
|
||||||
|
" 'model': best_model,\n",
|
||||||
|
" 'confusion_matrix': confusion_matrix(self.y_test, y_pred)\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
" metrics = {\n",
|
||||||
|
" 'Model': name,\n",
|
||||||
|
" 'Accuracy': accuracy_score(self.y_test, y_pred),\n",
|
||||||
|
" 'Precision': precision_score(self.y_test, y_pred, average='weighted', zero_division=0),\n",
|
||||||
|
" 'Recall': recall_score(self.y_test, y_pred, average='weighted'),\n",
|
||||||
|
" 'F1 Score': f1_score(self.y_test, y_pred, average='weighted'),\n",
|
||||||
|
" 'Best Params': grid_search.best_params_,\n",
|
||||||
|
" 'Training Time (s)': (end_train - start_train),\n",
|
||||||
|
" 'Prediction Time (s)': (end_pred - end_train),\n",
|
||||||
|
" 'Total Time (s)': (end_pred - start_train)\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
" results_list.append(metrics)\n",
|
||||||
|
"\n",
|
||||||
|
" self.results_df = pd.DataFrame(results_list)\n",
|
||||||
|
"\n",
|
||||||
|
" def print_summary(self):\n",
|
||||||
|
" print(\"\\n------ Results Sorted by Accuracy ------\")\n",
|
||||||
|
" print(self.results_df.sort_values(by='Accuracy', ascending=False).to_string(index=False))\n",
|
||||||
|
"\n",
|
||||||
|
" print(\"\\n------ Results Sorted by Total Time ------\")\n",
|
||||||
|
" print(self.results_df.sort_values(by='Total Time (s)', ascending=True).to_string(index=False))\n",
|
||||||
|
"\n",
|
||||||
|
" # Show feature importance for models that support it \n",
|
||||||
|
" def show_feature_importance(self):\n",
|
||||||
|
" importance = {}\n",
|
||||||
|
"\n",
|
||||||
|
" for name, result in self.models.items():\n",
|
||||||
|
" model = result['model']\n",
|
||||||
|
" if hasattr(model, 'feature_importances_'):\n",
|
||||||
|
" importance[name] = model.feature_importances_\n",
|
||||||
|
" elif hasattr(model, 'coef_'):\n",
|
||||||
|
" coef = model.coef_\n",
|
||||||
|
" if coef.ndim == 1:\n",
|
||||||
|
" importance[name] = np.abs(coef)\n",
|
||||||
|
" else:\n",
|
||||||
|
" importance[name] = np.mean(np.abs(coef), axis=0)\n",
|
||||||
|
" else:\n",
|
||||||
|
" print(f\"Feature importance not available for model {name}\")\n",
|
||||||
|
"\n",
|
||||||
|
" for name, imp in importance.items():\n",
|
||||||
|
" sorted_idx = np.argsort(imp)[::-1]\n",
|
||||||
|
" plt.figure()\n",
|
||||||
|
" plt.bar(range(len(imp)), imp[sorted_idx], align='center')\n",
|
||||||
|
" plt.xticks(range(len(imp)), sorted_idx)\n",
|
||||||
|
" plt.title(f\"Feature importance for {name}\")\n",
|
||||||
|
" plt.xlabel(\"Feature index\")\n",
|
||||||
|
" plt.ylabel(\"Importance score\")\n",
|
||||||
|
" plt.grid(True, linestyle='--', alpha=0.6)\n",
|
||||||
|
" plt.show()\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 12,
|
||||||
|
"id": "d6fd1fee",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"Tuning Decision Tree ...\n",
|
||||||
|
"Tuning Random Forest ...\n",
|
||||||
|
"\n",
|
||||||
|
"------ Results Sorted by Accuracy ------\n",
|
||||||
|
" Model Accuracy Precision Recall F1 Score Best Params Training Time (s) Prediction Time (s) Total Time (s)\n",
|
||||||
|
"Random Forest 0.857617 0.852147 0.857617 0.852588 {'max_depth': 16, 'min_samples_split': 5, 'n_estimators': 200} 303.932920 0.216372 304.149293\n",
|
||||||
|
"Decision Tree 0.846381 0.840787 0.846381 0.842219 {'max_depth': 10, 'min_samples_split': 9} 3.946769 0.001706 3.948475\n",
|
||||||
|
"\n",
|
||||||
|
"------ Results Sorted by Total Time ------\n",
|
||||||
|
" Model Accuracy Precision Recall F1 Score Best Params Training Time (s) Prediction Time (s) Total Time (s)\n",
|
||||||
|
"Decision Tree 0.846381 0.840787 0.846381 0.842219 {'max_depth': 10, 'min_samples_split': 9} 3.946769 0.001706 3.948475\n",
|
||||||
|
"Random Forest 0.857617 0.852147 0.857617 0.852588 {'max_depth': 16, 'min_samples_split': 5, 'n_estimators': 200} 303.932920 0.216372 304.149293\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"image/png": 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truncated
|
||||||
|
"text/plain": [
|
||||||
|
"<Figure size 640x480 with 1 Axes>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"image/png": 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truncated
|
||||||
|
"text/plain": [
|
||||||
|
"<Figure size 640x480 with 1 Axes>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"# ===== MAIN =====\n",
|
||||||
|
"# optimized classifiers and find their hyperparameters\n",
|
||||||
|
"clf_opt = ClassifierComparisonOpt(X_train, y_train)\n",
|
||||||
|
"clf_opt.fit_models()\n",
|
||||||
|
"clf_opt.print_summary()\n",
|
||||||
|
"\n",
|
||||||
|
"# Show feature importance for each model\n",
|
||||||
|
"clf_opt.show_feature_importance()"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"kernelspec": {
|
"kernelspec": {
|
||||||
"display_name": ".venv",
|
"display_name": "Python 3",
|
||||||
"language": "python",
|
"language": "python",
|
||||||
"name": "python3"
|
"name": "python3"
|
||||||
},
|
},
|
||||||
|
|||||||
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Reference in new issue
Block a user