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projekt_rozpoznywanie_grzybow/mushrooms.ipynb
s464968 6fd4e18560 Cleanup
2023-02-03 15:47:49 +01:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 165,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('mushrooms/train/train.tsv',sep='\\t', header=None)\n",
"X_ver = pd.read_csv('mushrooms/dev-0/in.tsv',sep='\\t', header=None)\n",
"y_ver = pd.read_csv('mushrooms/dev-0/expected.tsv',sep='\\t', header=None)"
]
},
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"cell_type": "code",
"execution_count": 167,
"metadata": {},
"outputs": [
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]
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],
"source": [
"print(df.head())\n",
"print(df.isna().sum())\n",
"print(df.shape)"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {},
"outputs": [
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{
"cell_type": "code",
"execution_count": 170,
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"execution_count": 170,
"metadata": {},
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],
"source": [
"y_ver = pd.get_dummies(y_ver,columns=[0],drop_first=True)\n",
"df2_unique = df[2].unique()\n",
"X_ver[1] = pd.Categorical(X_ver[1],categories=df2_unique)\n",
"X_ver = pd.get_dummies(X_ver)\n",
"X_ver"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {},
"outputs": [
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" </tbody>\n",
"</table>\n",
"<p>6465 rows × 118 columns</p>\n",
"</div>"
],
"text/plain": [
" 0_p 1_b 1_c 1_f 1_k 1_s 1_x 2_f 2_g 2_s ... 21_s 21_v 21_y \\\n",
"0 1 0 0 0 0 0 1 0 0 1 ... 1 0 0 \n",
"1 0 0 0 0 0 0 1 0 0 1 ... 0 0 0 \n",
"2 1 0 0 0 0 0 1 0 0 0 ... 1 0 0 \n",
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"4 0 0 0 0 0 0 1 0 0 0 ... 0 0 0 \n",
"... ... ... ... ... ... ... ... ... ... ... ... ... ... ... \n",
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"6463 1 0 0 0 1 0 0 0 0 0 ... 0 1 0 \n",
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"\n",
"[6465 rows x 118 columns]"
]
},
"execution_count": 171,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.get_dummies(df,columns=[0],drop_first=True)\n",
"df = pd.get_dummies(df)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(4848, 117)\n",
"(1617, 117)\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"X = df.loc[:, df.columns != '0_p']\n",
"y = df['0_p']\n",
"X_train,X_test,y_train,y_test = train_test_split(X,y)\n",
"print(X_train.shape)\n",
"print(X_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {},
"outputs": [
{
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],
"text/plain": [
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]
},
"execution_count": 173,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_ver.columns = X_test.columns\n",
"X_ver"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.00\n",
"1.00\n"
]
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"\n",
"poly = PolynomialFeatures(degree=2,include_bias=False)\n",
"X_poly = poly.fit_transform(X_train)\n",
"lr = LogisticRegression(C=10).fit(X_poly,y_train)\n",
"print('{:.2f}'.format(lr.score(X_poly,y_train)))\n",
"print('{:.2f}'.format(lr.score(poly.fit_transform(X_test),y_test)))"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.95\n",
"0.97\n"
]
}
],
"source": [
"from sklearn.naive_bayes import GaussianNB\n",
"\n",
"gnb = GaussianNB()\n",
"gnb.fit(X_train,y_train)\n",
"\n",
"print('{:.2f}'.format(gnb.score(X_train,y_train)))\n",
"print('{:.2f}'.format(gnb.score(X_test,y_test)))"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.00\n",
"1.00\n"
]
}
],
"source": [
"from sklearn.svm import SVC\n",
"\n",
"svc = SVC(kernel='rbf',C=10, gamma=0.1).fit(X_train,y_train)\n",
"\n",
"print('{:.2f}'.format(svc.score(X_train,y_train)))\n",
"print('{:.2f}'.format(svc.score(X_test,y_test)))"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/tonywesoly/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
" mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.00\n",
"1.00\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/tonywesoly/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
" mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
]
}
],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"\n",
"knn = KNeighborsClassifier(n_neighbors=3).fit(X_train,y_train)\n",
"print('{:.2f}'.format(knn.score(X_train,y_train)))\n",
"print('{:.2f}'.format(knn.score(X_test,y_test)))"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.00\n",
"1.00\n"
]
}
],
"source": [
"from sklearn.neural_network import MLPClassifier\n",
"\n",
"mlp = MLPClassifier(activation='relu', hidden_layer_sizes=[10],solver='lbfgs').fit(X_train,y_train)\n",
"\n",
"print('{:.2f}'.format(mlp.score(X_train,y_train)))\n",
"print('{:.2f}'.format(mlp.score(X_test,y_test)))"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bayes raport:\n",
"Accuracy score 0.91\n",
" precision recall f1-score support\n",
"\n",
" jadalne 1.00 0.82 0.90 406\n",
" trujące 0.84 1.00 0.91 386\n",
"\n",
" accuracy 0.91 792\n",
" macro avg 0.92 0.91 0.91 792\n",
"weighted avg 0.92 0.91 0.91 792\n",
"\n"
]
}
],
"source": [
"from sklearn.metrics import classification_report,accuracy_score\n",
"\n",
"pred_bayes = gnb.predict(X_ver)\n",
"print('Bayes raport:')\n",
"print('Accuracy score {:.2f}'.format(accuracy_score(y_ver,pred_bayes)))\n",
"print(classification_report(y_ver,pred_bayes,target_names=['jadalne','trujące']))"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Logistic Regression raport:\n",
"Accuracy score 1.00\n",
" precision recall f1-score support\n",
"\n",
" jadalne 1.00 1.00 1.00 406\n",
" trujące 1.00 1.00 1.00 386\n",
"\n",
" accuracy 1.00 792\n",
" macro avg 1.00 1.00 1.00 792\n",
"weighted avg 1.00 1.00 1.00 792\n",
"\n"
]
}
],
"source": [
"pred_log = lr.predict(poly.fit_transform(X_ver))\n",
"print('Logistic Regression raport:')\n",
"print('Accuracy score {:.2f}'.format(accuracy_score(y_ver,pred_log)))\n",
"print(classification_report(y_ver,pred_log,target_names=['jadalne','trujące']))"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Support Vector Machines raport:\n",
"Accuracy score 1.00\n",
" precision recall f1-score support\n",
"\n",
" jadalne 1.00 1.00 1.00 406\n",
" trujące 1.00 1.00 1.00 386\n",
"\n",
" accuracy 1.00 792\n",
" macro avg 1.00 1.00 1.00 792\n",
"weighted avg 1.00 1.00 1.00 792\n",
"\n"
]
}
],
"source": [
"pred_svc = svc.predict(X_ver)\n",
"print('Support Vector Machines raport:')\n",
"print('Accuracy score {:.2f}'.format(accuracy_score(y_ver,pred_svc)))\n",
"print(classification_report(y_ver,pred_svc,target_names=['jadalne','trujące']))"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"K-nearest neighbors raport:\n",
"Accuracy score 1.00\n",
" precision recall f1-score support\n",
"\n",
" jadalne 1.00 1.00 1.00 406\n",
" trujące 1.00 1.00 1.00 386\n",
"\n",
" accuracy 1.00 792\n",
" macro avg 1.00 1.00 1.00 792\n",
"weighted avg 1.00 1.00 1.00 792\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/tonywesoly/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
" mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
]
}
],
"source": [
"pred_knn = knn.predict(X_ver)\n",
"print('K-nearest neighbors raport:')\n",
"print('Accuracy score {:.2f}'.format(accuracy_score(y_ver,pred_knn)))\n",
"print(classification_report(y_ver,pred_knn,target_names=['jadalne','trujące']))"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Neural network raport:\n",
"Accuracy score 1.00\n",
" precision recall f1-score support\n",
"\n",
" jadalne 1.00 1.00 1.00 406\n",
" trujące 1.00 1.00 1.00 386\n",
"\n",
" accuracy 1.00 792\n",
" macro avg 1.00 1.00 1.00 792\n",
"weighted avg 1.00 1.00 1.00 792\n",
"\n"
]
}
],
"source": [
"pred_mlp = mlp.predict(X_ver)\n",
"print('Neural network raport:')\n",
"print('Accuracy score {:.2f}'.format(accuracy_score(y_ver,pred_mlp)))\n",
"print(classification_report(y_ver,pred_mlp,target_names=['jadalne','trujące']))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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": "3.9.13"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "fbbbb91f3443f337fad6219902aa19c75c8f48b69079f7de3a01210f85667a20"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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