From 33c35e12940dfc477d63983639caab6060895b00 Mon Sep 17 00:00:00 2001 From: Michel Penke Date: Fri, 12 Jul 2024 12:40:06 +0200 Subject: [PATCH] cleaning --- README.md | 12 +- example_files/train.py | 39 ++++- project_classification.ipynb | 284 ----------------------------------- requirements.txt | 3 +- 4 files changed, 50 insertions(+), 288 deletions(-) diff --git a/README.md b/README.md index c310f65..c5a2977 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,17 @@ ## Flight Prediction Test on Airport Data from Tunesian Airline -Based on several machine learning classifier this project tries to predict delays of individual airplanes. +Based on several machine learning classifier this project tries to predict delays of individual airplanes. + +### Set up the Presentation + +- Thre presentation can be started with streamlit. Make sure to have streamlit installed in your directory, as described in the requirements. + + ```BASH + streamlit run app.py + ``` + After that a local host is started in your standard browser. + ## Set up your Environment diff --git a/example_files/train.py b/example_files/train.py index ad43b1a..13bb507 100644 --- a/example_files/train.py +++ b/example_files/train.py @@ -194,7 +194,6 @@ duplicate_columns = df.columns[df.columns.duplicated()] df = df.loc[:, ~df.columns.duplicated()] -#Target engeneering # Convert target into certain category intervals def target_interval(row): @@ -212,7 +211,7 @@ def target_interval(row): return 6 df['target_cat'] = df.apply(target_interval, axis=1) - + # Standardization # Create a StandardScaler object @@ -231,7 +230,43 @@ def target_interval(row): X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.2, random_state=RSEED) # Train model +# Define the parameter distribution for random search +param_dist = { + 'n_estimators': randint(50, 100), # Reduced upper bound + 'learning_rate': uniform(0.01, 0.5), # Reduced upper bound + 'base_estimator__max_depth': randint(1, 5), # Reduced upper bound + 'base_estimator__min_samples_split': randint(2, 10), # Reduced upper bound + 'base_estimator__min_samples_leaf': randint(1, 10), # Reduced upper bound + 'algorithm': ['SAMME', 'SAMME.R'] +} +# Create a base model +base_estimator = DecisionTreeClassifier(random_state=RSEED) +ada = AdaBoostClassifier(base_estimator=base_estimator, random_state=RSEED) + +# Create a custom scorer (you can change this to other metrics if needed) +scorer = make_scorer(f1_score) + +# Instantiate RandomizedSearchCV object +random_search = RandomizedSearchCV( + estimator=ada, + param_distributions=param_dist, + n_iter=50, # Reduced number of iterations + cv=3, # Reduced number of cross-validation folds + scoring=scorer, + random_state=RSEED, + n_jobs=-1 # use all available cores +) + +# Fit RandomizedSearchCV +random_search.fit(X_train, y_train) + +# Print the best parameters and score +print("Best parameters:", random_search.best_params_) +print("Best cross-validation score:", random_search.best_score_) + +# Get the best model +model = random_search.best_estimator_ # Save the model dump(model, 'models/model.joblib') diff --git a/project_classification.ipynb b/project_classification.ipynb index 8552607..a69f525 100644 --- a/project_classification.ipynb +++ b/project_classification.ipynb @@ -225538,237 +225538,6 @@ "plt.show()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# per class" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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pIjs7u2x3i0lOTpaHh4dGjx6d7dLUa/tWqVJFd911l+rXry9vb2/99ttvCgoKkoeHR45jSpKHh0fRTAa4hqOjo+Wy5oCAAP3+++9699131aNHD2VkZOj8+fNWf+G+esxL0vLly3X58mVJUpkyZSRd+dy2t7fXXXfdZelTv359SVf+cFCvXr1cj3tXV1fLOMDN8PT0lJ+fn9W2+vXr6+uvv5b0/5+rycnJVr9jJCcnW+6o9/HHH1vO2Pvv70V5sXbtWj388MN65513FBoaarUvt2Ofz3sU1NVA6vDhw1q1apXVWR8eHh7Zbsry77//6ty5c5ZjLyEhwbLvv2eMzJ8/X88995wWLVpkdRlTfHy8Tp06pWbNmlm2ZWZmat26dZo2bZrS09Pl4eFxw58lwM26cOGCvvrqK33++edat26d2rZtq7CwMKufAydOnFD79u3VqlWrbDcSWrVqlZYuXapJkyZJunJ31qysLNnb22vGjBnq3bs3n9sm4kwp3FD9+vV19OhRHT161LJt165dOn/+fLZfAK/ts23bNqs72vz2229WbZo1a6adO3fKx8dHdevWtXqULVs225h79uzR2bNnNW7cOLVp00a+vr453v3swQcfVNmyZfXhhx9qxYoVXLqHAnF0dFRAQIDlclbpyl/kY2NjFRQUpGrVqlkdu7m5GvKmp6dLunLm4Lp16yz/sJeklStXql69elZ3iwLMcvVMp4CAADk4OFgd84mJiTpy5IjljNeaNWtajnkvLy9JUuvWrfXvv/9anel69VLsmjVrSrpy3F87rnTluP/vmbRAfrVu3TrbGXl79+61HHu1atWSh4eH1fGXmpqqjRs3Wo4/Ly8vy3F9tV9erVmzRp07d9b48eNzvLMZxz6KwtVAat++ffr5559VuXJlq/1BQUE6f/684uPjLdtWrVqlrKwsBQYGSpLV7zDXXur35ZdfKjw8XF9++aU6d+5sNW6HDh20fft2JSQkWB7NmzfX008/rYSEBNnZ2eXpZwmQH5mZmfrhhx/01FNPyd3dXePGjVOHDh104MABxcbGKjQ01LJW6/Hjx9WuXTsFBARo9uzZsrW1jj3i4uKsjt/Ro0erfPnySkhI0KOPPiqJz21TFfNC6ygB/nu3DMMwjC5duhhhYWGGYVy5Y42/v7/Rpk0bIz4+3ti4caMREBBgtG3bNtcxL1y4YFSpUsV45plnjJ07dxrff/+9UbduXau77x0/ftyoWrWq0a1bN2PTpk3G/v37jRUrVhjPPvus8e+//2ar7dSpU4ajo6MxdOhQ488//zS+/fZb46677sp2Rz/DMIzXXnvNcHR0NOrXr18IXyHc7ubPn284OTkZc+bMMXbt2mU8//zzRoUKFXK9A+Vvv/1mvP/++8bWrVuNQ4cOGbGxsUarVq2MOnXqGP/8849hGFfuSuPu7m706tXL2LFjhzF//nzDxcXF+Oijj8ycGm5Tw4cPN9auXWscPHjQ2LZtmzF8+HDDxsbG+OmnnwzDMIx+/foZNWrUMFatWmVs3rzZCAoKMoKCgq47ZmZmptGsWTPj3nvvNbZs2WJs3rzZCAwMNDp27Ghpc+DAAcPFxcUYOnSosXv3bmP69OmGnZ2dsWLFiiKdL0q/TZs2Gfb29sbYsWONffv2GXPnzjVcXFyML774wtJm3LhxRoUKFYxvv/3W2LZtm9GlSxejVq1axqVLl6479r59+4ytW7caL7zwgnHXXXcZW7duNbZu3Wq5e+qqVasMFxcXY8SIEcbJkyctj7Nnz1rG+PXXXw17e3tj0qRJxu7du42oqCjDwcHB2L59e9F8QVAqXLhwwXK8STKmTJlibN261Th8+LCRkZFhPPLII8Ydd9xhJCQkWB17197Zt1OnTkbTpk2NjRs3GuvXrzfuvPNOo2fPntd93blz5xr29vbG9OnTrcY9f/58rn1y+vfEzfwsAXIzevRow83NzXj++eeNX3/9Ndd2x44dM+rWrWt06NDBOHbsmNUxnJuc7r7H57Z5CKVww1DKMAzj8OHDxiOPPGKULVvWKF++vNG9e/dc/0F+VVxcnNGkSRPD0dHR8Pf3N77++utsAdLevXuNRx991KhQoYJRpkwZw9fX1xgyZIiRlZWVY23z5s0zfHx8DCcnJyMoKMhYunRpjqHUn3/+aUgyJkyYcDNfEiCb999/36hRo4bh6OhotGzZ0vjtt99ybbtt2zajffv2RqVKlQwnJyfDx8fH6Nevn3Hs2DGrdn/88Ydxzz33GE5OToaXl5cxbty4op4GYBiGYfTu3duoWbOm4ejoaFStWtXo0KGDJZAyDMO4dOmS0b9/f6NixYqGi4uL8eijj173l7mrjh8/bjz22GNGuXLlDHd3d+PZZ5+1+oe5YRjG6tWrDX9/f8PR0dGoXbu2MXv27MKeHm5Ty5YtMxo2bGg4OTkZvr6+xowZM6z2Z2VlGSNHjjTc3d0NJycno0OHDkZiYuINx23btq0hKdvj4MGDhmEYRlhYWI77//vHu4ULFxp33XWX4ejoaDRo0MD4/vvvC2vqKKVWr16d47EVFhZmHDx4MMd9kozVq1dbxjh79qzRs2dPo1y5coarq6sRHh5uXLhw4bqvm9sxf+2/DXLq899/T9zszxIgJwcPHrzhHxEM40rAlNv3xvX6/DeUMgw+t81iYxisuovS55dfflGHDh109OjRbHdNAAAAAAAAxY9QCqVKenq6Tp8+rbCwMHl4eGju3LnFXRIAAAAAAMgBC52jVPnyyy9Vs2ZNnT9/XhMmTCjucgAAAAAAQC44UwoAAAAAAACm40wpAAAAAAAAmI5QCgAAAAAAAKYjlAIAAAAAAIDpCKUAAAAAAABgOkIpAAAAAAAAmI5QCgAA3HbWrFkjGxsbrVmzxtTXnTNnjmxsbHTo0CFTX/d62rVrp3bt2hV3GQAA4DZEKAUAAEqkqwHO1Yezs7OqV6+ukJAQvffee7pw4UJxl4g8OnHihN58800lJCQUdykAAKAEsS/uAgAAAK5n9OjRqlWrli5fvqykpCStWbNGQ4YM0ZQpU7R06VI1bty4uEvMs169eunJJ5+Uk5NTcZdi8dNPPxX5a5w4cUKjRo2Sj4+P/P39i/z1AADArYFQCgAAlGgPPPCAmjdvbnk+YsQIrVq1Sg899JAeeeQR7d69W2XKlCnGCvPOzs5OdnZ2xV2GFUdHx+IuAQAA3Ka4fA8AANxy7rvvPo0cOVKHDx/WF198YbVvz5496tatmypVqiRnZ2c1b95cS5cuveGYv/zyi7p3764aNWrIyclJ3t7e+t///qdLly5Z2syePVs2NjbaunVrtv5vv/227OzsdPz48VxfI6c1pXx8fPTQQw9p/fr1atmypZydnVW7dm199tlnN6z50KFDsrGx0aRJkzR9+nTVrl1bLi4uuv/++3X06FEZhqExY8bojjvuUJkyZdSlSxedO3fOaoz/ril1db2thQsXauzYsbrjjjvk7OysDh06aP/+/VZ9fXx89Oyzz2ar69ox16xZoxYtWkiSwsPDLZdjzpkzx9J+48aN6tSpk9zc3OTi4qK2bdvq119/tRrzwoULGjJkiHx8fOTk5KRq1aqpY8eO2rJlyw2/TgAAoGQilAIAALekXr16SbK+/Gznzp26++67tXv3bg0fPlyTJ09W2bJl1bVrVy1ZsuS64y1atEh///23XnzxRb3//vsKCQnR+++/r9DQUEubbt26qUyZMpo7d262/nPnzlW7du3k5eWV77ns379f3bp1U8eOHTV58mRVrFhRzz77rHbu3Jmn/nPnztUHH3ygQYMG6eWXX9batWv1xBNP6I033tCKFSs0bNgwPf/881q2bJleeeWVPI05btw4LVmyRK+88opGjBih3377TU8//XS+51a/fn2NHj1akvT888/r888/1+eff657771XkrRq1Srde++9Sk1NVVRUlN5++22dP39e9913nzZt2mQZp1+/fvrwww/1+OOP64MPPtArr7yiMmXKaPfu3fmuCQAAlAxcvgcAAG5Jd9xxh9zc3PTnn39atg0ePFg1atTQ77//blm3qX///rrnnns0bNgwPfroo7mON378eKvLAJ9//nnVrVtXr732mo4cOaIaNWqofPny6tq1q7788ktNmDBBtrZX/r63detW7dq1S0OHDr2puSQmJmrdunVq06aNJOmJJ56Qt7e3Zs+erUmTJt2w//Hjx7Vv3z65ublJkjIzMxUdHa1Lly5p8+bNsre/8ivf6dOnNXfuXH344Yc3XNfqn3/+UUJCguXyvooVK2rw4MHasWOHGjZsmOe5ubu764EHHlBkZKSCgoL0zDPPWPYZhqF+/fqpffv2+uGHH2RjYyNJeuGFF9SgQQO98cYbltDx+++/V9++fTV58mRL/1dffTXPdQAAgJKHM6UAAMAtq1y5cpa78J07d06rVq3SE088oQsXLujMmTM6c+aMzp49q5CQEO3bt++6l9ZdG0ilpaXpzJkzatWqlQzDsLpcLzQ0VCdOnNDq1ast2+bOnasyZcro8ccfv6l5+Pn5WQIpSapatarq1aunAwcO5Kl/9+7dLYGUJAUGBkqSnnnmGUsgdXV7RkbGdb8OV4WHh1utN3W1vrzWlBcJCQnat2+fnnrqKZ09e9bynqWlpalDhw5at26dsrKyJEkVKlTQxo0bdeLEiUJ7fQAAULw4UwoAANyyLl68qGrVqkm6cgmcYRgaOXKkRo4cmWP7U6dO5Xp53ZEjRxQZGamlS5fqr7/+stqXkpJi+f+OHTvK09NTc+fOVYcOHZSVlaUvv/xSXbp0Ufny5W9qHjVq1Mi2rWLFitnqyGv/qwGVt7d3jtvzMu5/x6xYsWKe++bVvn37JElhYWG5tklJSVHFihU1YcIEhYWFydvbWwEBAXrwwQcVGhqq2rVrF1o9AADAXIRSAADglnTs2DGlpKSobt26kmQ5o+aVV15RSEhIjn2utv2vzMxMdezYUefOndOwYcPk6+ursmXL6vj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uLi7q379/vmqrXbu2QkJCsrVnLUB+4MCBfM1XnHbu3KklS5Zo586dOnnypDIzMyVJqampki68JsHBwZKkli1bytXVVdOmTVPDhg115513qmbNmrnOnZ/3VHE7f/68Zs6cKUnq16+f07aYmBiNHDlSK1eu1B9//KH69es7tmUd6/fee2+O88bGxuqNN97I9XmPHTumr7/+Whs3btRff/2lc+fOSbqw1pakXNfuypr74voPHDigX375RVOnTpWrq6vGjx8vNze3bLXm5325f/9+/fHHH7mOs9lsiouL0+OPP67ly5dr5MiRki58b9955x0NHz5cxhjdcsstqlixYq77UlhZx2PVqlWLZD673S5J+VrTqiRl7XfW6wAAyD9CKQDAJd1///3Futh1TnNXq1bNsSDv0KFDtWrVKn3//ffq1KmT3NzcFBISoptuukl33323rr32Wse4Y8eO6fTp05KU50Wls4KIrAV//83X11dVqlTR8ePHtX///mxBTlBQUI7j/vzzT0kX7sR1uT+wjhw5kqdaL6e49iU/hg8fnm3h9Ut59tln9eKLLzpCgZxcfCeurLsP1qxZM98BWu3atXNs9/b2lnRhsfC8ql69uiTp8OHD+arhcjIzMzVo0CC9++67Msbk2u/i16R+/fp6/fXXNXToUA0aNEiDBg1SnTp1FBERodtvv129evWSu7u7o39+3lOXcvToUT355JPZ2hs1aqThw4fnaY6vv/5aKSkpCggIUFRUlNM2Pz8/3XbbbVqwYIGmTZvmWJxakvbv3y8p92M9t3ZJmjp1qh5//HHHZ0VOLnX3txkzZmRrO3TokDp16qQ333xTdrtdb731lmNbQd6XWWOqVq3qOD7/LSukuzhM7dOnj5YuXapZs2apR48ecnV1VXBwsG688Ub17NlTN998c677VRBpaWmSlGuN+XX06FFJuuyNAy5l3Lhx2rp1a7b28ePHq1q1ao737vHjx5WZmZntrnv5kbXff/31V4HnAIArHaEUAKBEZZ0lcbE6deo4QikvLy8tXbpUv/76q5YsWaKff/5ZP//8s9auXasJEyZo4MCBmjRpktVlO5QvXz7H9qx/8W/QoIFuuOGGS85h1Vlnl5PbvhSXL774QmPGjFHFihX19ttv6+abb1atWrVUvnx52Ww2jRw5UmPHjr1kOJMfLi5Fd9PhsLAwffTRR1q3bl2h/7C92BtvvKEpU6bI399fEyZM0PXXXy8/Pz/HmUz33HOPPvnkk2yvyeDBg3XXXXdpwYIFWrVqlVatWqU5c+Zozpw5Gj16tH788UfH2VNF9Z46depUju/ftm3b5jmUyrq73j///KO2bdtm254VuMyYMUPPPfdcoV/npKQkPfjgg3J1ddXLL7+sLl26qHbt2vLy8pLNZtN7772nBx98MN/HXM2aNfXCCy+oa9eumjx5sl544YVCn3VYEC4uLvr44481cuRIff311/rpp5/0008/afLkyZo8ebK6dOmi+fPnF9nx6uvrK+nSIV5+rFu3TpLUrFmzAs+xZMkS/fDDD9nax4wZo2rVqiksLEySlJGRod9++00tW7Ys8HNlhXKVK1cu8BwAcKUjlAIAlKi8/vF37bXXOs7gOH/+vL788kvFxMTonXfeUc+ePdW+fXtVrVpVXl5e+vvvv7Vt2zY1bdr0svMGBARo69atjjOb/i0tLU3Hjx939M2rwMBASRfO2MrpzIriUFz7Ulw+/fRTSdKLL76Y4+WWWZdSXSzrbKdDhw4pLS2tRP7wl6Tbb79d8fHxOnHihBYsWKDu3bsXybxZr8m7776rrl27Ztue02uSxc/PTwMGDNCAAQMkSVu3blW/fv2UmJio4cOHZwuQ8vKeupSgoKBCBYaHDh3S4sWLJV04y/Gnn37Kte/Bgwe1ZMkSde7cWdL/jvXdu3fn2D+39nnz5skYo8GDB+upp57Ktv1Sr+/l1KtXT9KFs9127NihVq1aOdWan/dl1n+PHTum9PT0HM9Eypovp/dycHCwgoODNXToUBljtGzZMt1zzz1auHChPvzwQ8XFxRV4Py9Wo0YNR52FdfToUX377beSpFtuuaXA82RdLpmb5s2bq27dutq1a5dmzpxZqFAqa7/9/PwKPAcAXOmK7p8MAQCwSLly5dSzZ0/H5T7JycmSJFdXV3Xs2FHShUt08iLrUrOczviQpGnTpkmSrr766nwFOR06dJC7u7tWrFhR5Jd45aa49qW4ZP0hXqdOnWzbDh8+7Fgf7GL+/v4KCQmR3W537E9JqF+/vnr37i1JeuKJJxz7kpvDhw9fcp2iLJd6TTZt2uQ41vOiUaNGGjZsmCRddlxu76niNGPGDGVmZio8PFzmws13cnxkhUdZZ1VJcpxVNWvWrBzn/vDDD3Nsv9Tr+88//+jzzz8v8P5krQElyWkdp4K8L6+66irH5Xk5hdrGGEf75cJDm82mDh066J577pHk/L3Nuqwzax23/MoKdLZs2VKg8VmMMRo0aJDOnDmjKlWq5Hu9uPzIOgtTkiZPnqw1a9Zcsv/58+f1yy+/ZGu32+2O/c46+woAkH+EUgCAUu2dd97J8Y/5lJQUrV27VpLzH5j/93//p3Llyuntt9/WO++8k+1Mjj179igpKcnx9YABA+Tt7a1169bppZdecuq/fv16vfDCC5IurMOTH35+fho8eLBOnz6tLl26aMOGDdn6nD17VgsWLMhx/ZOCKK59KS5ZC4y/9957ysjIcLSnpaUpNjbWcWnMv40ePVrShe91TiHC5s2bC/1Hcl689dZbatCggXbt2qUbb7xRq1atytYnIyND06ZNU4sWLfJUU9ZrMmnSJMcloNKFs4piYmJyDA+WLVumxYsXZ1uXyxijRYsWSXJ+j+T3PVVcssKY3BYAzxITEyNJWrRokWP9tf79+6tixYpKTEzUm2++6dR/xYoVmjJlSo5zZb2+M2fO1MmTJx3t//zzjwYOHKhdu3YVaF8OHTqkZ555RtKFsyMvviS3oO/LrPW6nn/+ef3222+OdmOMXnjhBSUnJ8vX19dxZpx0IYy7+PMty8mTJx1nEF38va1evbrc3d2VkpJy2WA1J/Xq1VPt2rV15MgR7dy5M9/jJen333/Xbbfdprlz58rV1VUff/yxKlWqVKC58ur+++9Xz549de7cOXXs2FEzZ8503EwgS9YZZtdff73mzJmTbY5NmzYpLS1NDRs2LBUhPwD8Z1l2nz8AwH9KQW65PXXqVBMeHu54uLu7G0kmODjY0datW7d81RESEmIkmbp165ouXbqYe++919xyyy2mfPnyRpK5+eabzblz55zGzJw507i5uTludd6zZ09z5513mtDQUGOz2czo0aOd+i9cuNB4enoaSaZRo0amd+/epkOHDqZcuXJGkomLi8tW1+jRo42kbHNd7Ny5c+aee+5x3DK+RYsWpkePHiY6OtrccMMNpkKFCkaS+eabb/L8emTdXj2370tx7cvlSDKSTFRUlImNjc318e233zrG/Pnnn8bX19dIMgEBAaZHjx6ma9euxsfHx9SsWdP069cv17pefPFFY7PZHPsZHR1tunbtaoKDg7O9PsuXLzeSTNu2bS9bf36lpqaadu3aOcbXrVvX3HHHHaZ3797m5ptvNhUrVjSSjLe3t1m9erVj3PTp040kExsb6zTfL7/84njfNGjQwNx1112mU6dOpnz58qZJkyame/fu2fbv9ddfdzxHu3btzD333GO6d+/ueA/7+PiY9evXO/oX5D1V1FasWGEkGQ8PD3P8+PHL9m/ZsqWRZMaPH+9o++STT4yrq6uRZJo1a2Z69+5tbrrpJmOz2czjjz+e4/f0r7/+crwuVatWNd26dTM9evQwNWrUMJUqVTJDhgzJ8fuSdQxlbct63HvvvaZ9+/aO91zlypXNmjVrstVfkPel3W43ffr0MZJMuXLlTIcOHUzv3r3NNddcYySZ8uXLm8WLFzuNueOOO4wkU6tWLXPbbbeZe++919x2223Gx8fHSDJNmzY16enpTmN69uxpJJnAwEDTu3dv079/f9O/f//Lfk+yPProo0aSeeedd3LcnvWZVb9+fafXrXPnzo7vRdbxuGzZsjw9Z0F+Pv1bRkaGGTRokONzpGrVqqZTp07mnnvuMZ07dzY1a9Y0koyrq6uZNGlStvETJkwwksxTTz1V4BoAABdOiwYAIJuC/NKfFW5c6lGnTp181bFo0SLz8MMPmxYtWpjq1asbd3d3c9VVV5l27dqZmTNnmoyMjBzHbdq0yfTv39/UrVvXeHh4GB8fHxMcHGwGDRpkNm3alK3/5s2bTWxsrLnqqquMm5ub8fX1Ne3btzdz5sy55L7mJchZvHixufPOO01AQIBj7saNG5u7777bzJ4925w+fTrPr8flQqni3pfcXO77nvV4/fXXncbt2rXL3HvvvaZ27drGw8PD1KlTxzz00EMmJSXlsnUlJiaa3r17O17XKlWqmJCQEPPUU0+ZPXv2OPoVZyiV5ZtvvjExMTGmQYMGpmLFisbNzc34+/ubjh07mokTJ5pjx4459c8tlDLGmN9//9107drV1KxZ03h6epqrr77aPPXUUyY9PT3H7//OnTvNmDFjTIcOHUzt2rWNp6enqVy5smnevLkZPny42bdvn9P8BX1PFaWssKVnz5556j9x4kQjyTRu3Nip/ccffzRRUVHG29vbeHl5mRYtWph3333XGJP79/TIkSNm4MCBpn79+sbDw8PUqlXL3HfffWbHjh25fl8uDqUufthsNlOxYkUTGhpqhg0bZg4dOpTrPuT3fZll9uzZpl27dsbX19e4ubmZwMBA07dvX7N169ZsfVeuXGkee+wx07p1a+Pv72/c3d2Nv7+/iYiIMG+99ZY5depUtjHHjh0zDz74oKldu7YjzM/Pe2Hbtm3GZrOZ1q1b57g965i9+OHh4WFq1KhhwsLCzIABA8xXX32VryC0KEKpLJs2bTJDhgwxISEhxtfX15QrV85UrlzZhIe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", 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Custom labels for x-axis\n", - "custom_labels = {\n", - " '1': 'no delay',\n", - " '2': '0-30',\n", - " '3': '30-60',\n", - " '4': '60-120',\n", - " '5': '120-240',\n", - " '6': '>240'\n", - "}\n", - "\n", - "def plot_single_metric(df, metric, title_suffix):\n", - " plt.figure(figsize=(12, 6))\n", - " sns.barplot(x=df.index, y=metric, data=df)\n", - " \n", - " plt.title(f'{metric.capitalize()} for Each Class - {title_suffix}', fontsize=16)\n", - " plt.xlabel('Delay in minutes', fontsize=12)\n", - " plt.ylabel(metric.capitalize(), fontsize=12)\n", - " \n", - " # Center x-axis labels\n", - " plt.xticks(ha='center')\n", - " \n", - " # Add value labels on top of each bar\n", - " for i, v in enumerate(df[metric]):\n", - " plt.text(i, v, f'{v:.2f}', ha='center', va='bottom')\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "def plot_classification_metrics(y_true, y_pred, title_suffix):\n", - " # Get the classification report as a dict\n", - " report = classification_report(y_true, y_pred, output_dict=True)\n", - "\n", - " # Convert the dictionary to a DataFrame, excluding the 'accuracy', 'macro avg', and 'weighted avg' rows\n", - " df = pd.DataFrame(report).transpose()\n", - " df = df.drop(['accuracy', 'macro avg', 'weighted avg'])\n", - "\n", - " # Rename the index using custom labels\n", - " df.index = df.index.map(lambda x: custom_labels.get(x, x))\n", - "\n", - " metrics = ['f1-score']\n", - "\n", - " for metric in metrics:\n", - " plot_single_metric(df, metric, title_suffix)\n", - "\n", - "# Plot for training data\n", - "#plot_classification_metrics(y_train, y_pred_train_1, \"Best Model (Train)\")\n", - "\n", - "# Plot for training data\n", - "#plot_classification_metrics(y_train, y_pred_train_2, \"Best Short Model (Train)\")\n", - "\n", - "# Plot for training data\n", - "#plot_classification_metrics(y_train, y_pred_train_3, \"Best Long Model (Train)\")\n", - "\n", - "# Plot for test data\n", - "plot_classification_metrics(y_test, y_pred_test_0, \"LR-C\")\n", - "\n", - "# Plot for test data\n", - "plot_classification_metrics(y_test, y_pred_test_1, \"SGD-C\")\n", - "\n", - "# Plot for test data\n", - "plot_classification_metrics(y_test, y_pred_test_2, \"KNN-C\")\n", - "\n", - "# Plot for test data\n", - "plot_classification_metrics(y_test, y_pred_test_3, \"DT\")\n", - "\n", - "# Plot for test data\n", - "plot_classification_metrics(y_test, y_pred_test_4, \"RF\")\n", - "\n", - "# Plot for test data\n", - "plot_classification_metrics(y_test, y_pred_test_5, \"XGB\")\n", - "\n", - "# Plot for test data\n", - "plot_classification_metrics(y_test, y_pred_test_6, \"AdaBoost (DT-C)\")\n", - "\n", - "# Plot for test data\n", - "plot_classification_metrics(y_test, y_pred_test_7, \"Bagging (DT-C)\")\n", - "\n", - "# Plot for test data\n", - "plot_classification_metrics(y_test, y_pred_test_8, \"Extra Tree\")\n", - "\n", - "# Plot for test data\n", - "plot_classification_metrics(y_test, y_pred_test_9, \"GradientBoosting\")\n", - "\n", - "# Plot for test data\n", - "plot_classification_metrics(y_test, y_pred_test_10, \"Stacking (2-9)\")\n", - "\n", - "# Plot for test data\n", - "plot_classification_metrics(y_test, y_pred_test_11, \"MaxVoting (2-9)\")\n", - "\n", - "# # Plot for test data\n", - "# plot_classification_metrics(y_test, y_pred_test_3, \"Best Long Model (Test)\")" - ] - }, { "cell_type": "code", "execution_count": 148, @@ -225842,59 +225611,6 @@ "plot_all_classification_metrics(y_test, predictions, titles)\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df.head()" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/requirements.txt b/requirements.txt index 5b7e8d1..332d861 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,4 +9,5 @@ jupyterlab-dash==0.1.0a3 scikit-learn==1.2.2 statsmodels==0.13.5 pytest==7.3.1 -xgboost==1.24.3 \ No newline at end of file +xgboost==1.24.3 +streamlit==1.36.0 \ No newline at end of file