WaterQuality

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/shreshthvashisht/waterquality?scriptVersionId=215309369\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","execution_count":1,"id":"7cb703c4","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:30.204857Z","iopub.status.busy":"2024-12-29T16:39:30.204466Z","iopub.status.idle":"2024-12-29T16:39:35.005479Z","shell.execute_reply":"2024-12-29T16:39:35.004545Z"},"papermill":{"duration":4.815097,"end_time":"2024-12-29T16:39:35.007201","exception":false,"start_time":"2024-12-29T16:39:30.192104","status":"completed"},"tags":[]},"outputs":[],"source":["import torch\n","import torch.nn as nn\n","from torch.optim import Adam\n","from torch.nn import Linear, ReLU, BCELoss, Sequential,MSELoss,CrossEntropyLoss, LeakyReLU, BatchNorm1d, Dropout, Softmax\n","from torch.utils.data import TensorDataset,DataLoader\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import mean_squared_error,f1_score\n","from sklearn.preprocessing import LabelEncoder,StandardScaler\n","import warnings\n","warnings.filterwarnings(\"ignore\")"]},{"cell_type":"code","execution_count":2,"id":"32686821","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:35.024901Z","iopub.status.busy":"2024-12-29T16:39:35.024515Z","iopub.status.idle":"2024-12-29T16:39:35.146388Z","shell.execute_reply":"2024-12-29T16:39:35.145471Z"},"papermill":{"duration":0.132543,"end_time":"2024-12-29T16:39:35.147988","exception":false,"start_time":"2024-12-29T16:39:35.015445","status":"completed"},"tags":[]},"outputs":[],"source":["df=pd.read_csv('/kaggle/input/wellwaterquality/WknaT6YxR02YeXYuMDPg_water_quality.csv')"]},{"cell_type":"code","execution_count":3,"id":"be30b8b7","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:35.165129Z","iopub.status.busy":"2024-12-29T16:39:35.164854Z","iopub.status.idle":"2024-12-29T16:39:35.195721Z","shell.execute_reply":"2024-12-29T16:39:35.194904Z"},"papermill":{"duration":0.040718,"end_time":"2024-12-29T16:39:35.196999","exception":false,"start_time":"2024-12-29T16:39:35.156281","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Well_ID</th>\n","      <th>State</th>\n","      <th>District</th>\n","      <th>Block</th>\n","      <th>Village</th>\n","      <th>Latitude</th>\n","      <th>Longitude</th>\n","      <th>Year</th>\n","      <th>pH</th>\n","      <th>EC</th>\n","      <th>...</th>\n","      <th>NO3</th>\n","      <th>TH</th>\n","      <th>Ca</th>\n","      <th>Mg</th>\n","      <th>Na</th>\n","      <th>K</th>\n","      <th>F</th>\n","      <th>TDS</th>\n","      <th>WQI</th>\n","      <th>Water Quality Classification</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>W232200071580001</td>\n","      <td>Gujarat</td>\n","      <td>Ahmedabad</td>\n","      <td>Mandal</td>\n","      <td>Dalod</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2020</td>\n","      <td>8.20</td>\n","      <td>16640.0</td>\n","      <td>...</td>\n","      <td>26.00</td>\n","      <td>1451.0</td>\n","      <td>152.0</td>\n","      <td>260.0</td>\n","      <td>3535.0</td>\n","      <td>45.0</td>\n","      <td>1.00</td>\n","      <td>11149</td>\n","      <td>4361.44080</td>\n","      <td>Unsuitable for Drinking</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>W254029084355301</td>\n","      <td>Himachal Pradesh</td>\n","      <td>Solan</td>\n","      <td>Nallagarh</td>\n","      <td>JAGATPUR</td>\n","      <td>31.1594</td>\n","      <td>76.678500</td>\n","      <td>2019</td>\n","      <td>8.44</td>\n","      <td>299.0</td>\n","      <td>...</td>\n","      <td>2.70</td>\n","      <td>84.0</td>\n","      <td>17.0</td>\n","      <td>10.0</td>\n","      <td>39.0</td>\n","      <td>2.4</td>\n","      <td>0.20</td>\n","      <td>262</td>\n","      <td>85.80466</td>\n","      <td>Good</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>W193530074180001</td>\n","      <td>Maharashtra</td>\n","      <td>Ahmednagar</td>\n","      <td>SANGAMNER</td>\n","      <td>Kokangaon</td>\n","      <td>74.3000</td>\n","      <td>19.591667</td>\n","      <td>2022</td>\n","      <td>7.90</td>\n","      <td>1315.0</td>\n","      <td>...</td>\n","      <td>18.20</td>\n","      <td>465.0</td>\n","      <td>80.2</td>\n","      <td>64.4</td>\n","      <td>88.3</td>\n","      <td>1.6</td>\n","      <td>0.53</td>\n","      <td>372</td>\n","      <td>280.04130</td>\n","      <td>Very Poor yet Drinkable</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>W231620072033001</td>\n","      <td>Gujarat</td>\n","      <td>Ahmedabad</td>\n","      <td>Viramgam</td>\n","      <td>Endla</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2020</td>\n","      <td>7.40</td>\n","      <td>715.0</td>\n","      <td>...</td>\n","      <td>0.23</td>\n","      <td>280.0</td>\n","      <td>56.0</td>\n","      <td>34.0</td>\n","      <td>47.0</td>\n","      <td>11.0</td>\n","      <td>0.46</td>\n","      <td>479</td>\n","      <td>195.11649</td>\n","      <td>Poor</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>W251908084361501</td>\n","      <td>Himachal Pradesh</td>\n","      <td>Solan</td>\n","      <td>Nallagarh</td>\n","      <td>BARUNA</td>\n","      <td>31.1540</td>\n","      <td>76.638400</td>\n","      <td>2019</td>\n","      <td>8.40</td>\n","      <td>270.0</td>\n","      <td>...</td>\n","      <td>10.00</td>\n","      <td>116.0</td>\n","      <td>10.0</td>\n","      <td>18.0</td>\n","      <td>23.0</td>\n","      <td>1.0</td>\n","      <td>0.12</td>\n","      <td>262</td>\n","      <td>81.77860</td>\n","      <td>Good</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 24 columns</p>\n","</div>"],"text/plain":["            Well_ID             State    District      Block    Village  \\\n","0  W232200071580001           Gujarat   Ahmedabad     Mandal      Dalod   \n","1  W254029084355301  Himachal Pradesh       Solan  Nallagarh   JAGATPUR   \n","2  W193530074180001       Maharashtra  Ahmednagar  SANGAMNER  Kokangaon   \n","3  W231620072033001           Gujarat   Ahmedabad   Viramgam      Endla   \n","4  W251908084361501  Himachal Pradesh       Solan  Nallagarh     BARUNA   \n","\n","   Latitude  Longitude  Year    pH       EC  ...    NO3      TH     Ca     Mg  \\\n","0       NaN        NaN  2020  8.20  16640.0  ...  26.00  1451.0  152.0  260.0   \n","1   31.1594  76.678500  2019  8.44    299.0  ...   2.70    84.0   17.0   10.0   \n","2   74.3000  19.591667  2022  7.90   1315.0  ...  18.20   465.0   80.2   64.4   \n","3       NaN        NaN  2020  7.40    715.0  ...   0.23   280.0   56.0   34.0   \n","4   31.1540  76.638400  2019  8.40    270.0  ...  10.00   116.0   10.0   18.0   \n","\n","       Na     K     F    TDS         WQI  Water Quality Classification  \n","0  3535.0  45.0  1.00  11149  4361.44080       Unsuitable for Drinking  \n","1    39.0   2.4  0.20    262    85.80466                          Good  \n","2    88.3   1.6  0.53    372   280.04130       Very Poor yet Drinkable  \n","3    47.0  11.0  0.46    479   195.11649                          Poor  \n","4    23.0   1.0  0.12    262    81.77860                          Good  \n","\n","[5 rows x 24 columns]"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"markdown","id":"6d2f1b15","metadata":{"papermill":{"duration":0.007528,"end_time":"2024-12-29T16:39:35.212397","exception":false,"start_time":"2024-12-29T16:39:35.204869","status":"completed"},"tags":[]},"source":["# Data Pre-Processing"]},{"cell_type":"code","execution_count":4,"id":"5a057a94","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:35.2289Z","iopub.status.busy":"2024-12-29T16:39:35.228653Z","iopub.status.idle":"2024-12-29T16:39:35.240255Z","shell.execute_reply":"2024-12-29T16:39:35.239468Z"},"papermill":{"duration":0.021008,"end_time":"2024-12-29T16:39:35.241464","exception":false,"start_time":"2024-12-29T16:39:35.220456","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Well_ID                         3785\n","State                              0\n","District                           0\n","Block                           1119\n","Village                            1\n","Latitude                         389\n","Longitude                        390\n","Year                               0\n","pH                                 0\n","EC                                 0\n","CO3                                0\n","HCO3                               0\n","Cl                                 0\n","SO4                                0\n","NO3                                0\n","TH                                 0\n","Ca                                 0\n","Mg                                 0\n","Na                                 0\n","K                                  0\n","F                                  0\n","TDS                                0\n","WQI                                0\n","Water Quality Classification       0\n","dtype: int64"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["df.isnull().sum()"]},{"cell_type":"code","execution_count":5,"id":"ab9b8c1c","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:35.258171Z","iopub.status.busy":"2024-12-29T16:39:35.257937Z","iopub.status.idle":"2024-12-29T16:39:35.935613Z","shell.execute_reply":"2024-12-29T16:39:35.9348Z"},"papermill":{"duration":0.687567,"end_time":"2024-12-29T16:39:35.937111","exception":false,"start_time":"2024-12-29T16:39:35.249544","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["<Figure size 2200x21200 with 0 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"image/png":"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\n","text/plain":["<Figure size 500x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(22,212))\n","sns.catplot(x=df['Water Quality Classification'],y=df.WQI,hue=df['Water Quality Classification'])\n","plt.xticks(rotation=45)\n","plt.show()"]},{"cell_type":"markdown","id":"42282bc4","metadata":{"papermill":{"duration":0.009043,"end_time":"2024-12-29T16:39:35.955357","exception":false,"start_time":"2024-12-29T16:39:35.946314","status":"completed"},"tags":[]},"source":["Since the physical and chemical properties are the ones which will decide the property, therefore dropping the categorical columns and longitude, latitude and year"]},{"cell_type":"code","execution_count":6,"id":"10502e13","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:35.97425Z","iopub.status.busy":"2024-12-29T16:39:35.974026Z","iopub.status.idle":"2024-12-29T16:39:35.979983Z","shell.execute_reply":"2024-12-29T16:39:35.979177Z"},"papermill":{"duration":0.017052,"end_time":"2024-12-29T16:39:35.981374","exception":false,"start_time":"2024-12-29T16:39:35.964322","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Index(['Well_ID', 'State', 'District', 'Block', 'Village',\n","       'Water Quality Classification'],\n","      dtype='object')"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["df.select_dtypes(include='O').columns"]},{"cell_type":"code","execution_count":7,"id":"3204723c","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.001114Z","iopub.status.busy":"2024-12-29T16:39:36.000906Z","iopub.status.idle":"2024-12-29T16:39:36.005204Z","shell.execute_reply":"2024-12-29T16:39:36.004558Z"},"papermill":{"duration":0.015107,"end_time":"2024-12-29T16:39:36.006344","exception":false,"start_time":"2024-12-29T16:39:35.991237","status":"completed"},"tags":[]},"outputs":[],"source":["df.drop(['Well_ID', 'State', 'District', 'Block', 'Village','Latitude','Longitude','Year'],axis=1,inplace=True)"]},{"cell_type":"code","execution_count":8,"id":"da11915e","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.024746Z","iopub.status.busy":"2024-12-29T16:39:36.024549Z","iopub.status.idle":"2024-12-29T16:39:36.040923Z","shell.execute_reply":"2024-12-29T16:39:36.04028Z"},"papermill":{"duration":0.026789,"end_time":"2024-12-29T16:39:36.042111","exception":false,"start_time":"2024-12-29T16:39:36.015322","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>pH</th>\n","      <th>EC</th>\n","      <th>CO3</th>\n","      <th>HCO3</th>\n","      <th>Cl</th>\n","      <th>SO4</th>\n","      <th>NO3</th>\n","      <th>TH</th>\n","      <th>Ca</th>\n","      <th>Mg</th>\n","      <th>Na</th>\n","      <th>K</th>\n","      <th>F</th>\n","      <th>TDS</th>\n","      <th>WQI</th>\n","      <th>Water Quality Classification</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>8.20</td>\n","      <td>16640.0</td>\n","      <td>0.0</td>\n","      <td>1257.0</td>\n","      <td>5176.0</td>\n","      <td>822.0</td>\n","      <td>26.00</td>\n","      <td>1451.0</td>\n","      <td>152.0</td>\n","      <td>260.0</td>\n","      <td>3535.0</td>\n","      <td>45.0</td>\n","      <td>1.00</td>\n","      <td>11149</td>\n","      <td>4361.44080</td>\n","      <td>Unsuitable for Drinking</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>8.44</td>\n","      <td>299.0</td>\n","      <td>43.0</td>\n","      <td>87.0</td>\n","      <td>21.0</td>\n","      <td>0.0</td>\n","      <td>2.70</td>\n","      <td>84.0</td>\n","      <td>17.0</td>\n","      <td>10.0</td>\n","      <td>39.0</td>\n","      <td>2.4</td>\n","      <td>0.20</td>\n","      <td>262</td>\n","      <td>85.80466</td>\n","      <td>Good</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>7.90</td>\n","      <td>1315.0</td>\n","      <td>0.0</td>\n","      <td>518.7</td>\n","      <td>120.5</td>\n","      <td>61.6</td>\n","      <td>18.20</td>\n","      <td>465.0</td>\n","      <td>80.2</td>\n","      <td>64.4</td>\n","      <td>88.3</td>\n","      <td>1.6</td>\n","      <td>0.53</td>\n","      <td>372</td>\n","      <td>280.04130</td>\n","      <td>Very Poor yet Drinkable</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>7.40</td>\n","      <td>715.0</td>\n","      <td>0.0</td>\n","      <td>354.0</td>\n","      <td>50.0</td>\n","      <td>18.0</td>\n","      <td>0.23</td>\n","      <td>280.0</td>\n","      <td>56.0</td>\n","      <td>34.0</td>\n","      <td>47.0</td>\n","      <td>11.0</td>\n","      <td>0.46</td>\n","      <td>479</td>\n","      <td>195.11649</td>\n","      <td>Poor</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>8.40</td>\n","      <td>270.0</td>\n","      <td>43.0</td>\n","      <td>87.0</td>\n","      <td>14.0</td>\n","      <td>0.0</td>\n","      <td>10.00</td>\n","      <td>116.0</td>\n","      <td>10.0</td>\n","      <td>18.0</td>\n","      <td>23.0</td>\n","      <td>1.0</td>\n","      <td>0.12</td>\n","      <td>262</td>\n","      <td>81.77860</td>\n","      <td>Good</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     pH       EC   CO3    HCO3      Cl    SO4    NO3      TH     Ca     Mg  \\\n","0  8.20  16640.0   0.0  1257.0  5176.0  822.0  26.00  1451.0  152.0  260.0   \n","1  8.44    299.0  43.0    87.0    21.0    0.0   2.70    84.0   17.0   10.0   \n","2  7.90   1315.0   0.0   518.7   120.5   61.6  18.20   465.0   80.2   64.4   \n","3  7.40    715.0   0.0   354.0    50.0   18.0   0.23   280.0   56.0   34.0   \n","4  8.40    270.0  43.0    87.0    14.0    0.0  10.00   116.0   10.0   18.0   \n","\n","       Na     K     F    TDS         WQI Water Quality Classification  \n","0  3535.0  45.0  1.00  11149  4361.44080      Unsuitable for Drinking  \n","1    39.0   2.4  0.20    262    85.80466                         Good  \n","2    88.3   1.6  0.53    372   280.04130      Very Poor yet Drinkable  \n","3    47.0  11.0  0.46    479   195.11649                         Poor  \n","4    23.0   1.0  0.12    262    81.77860                         Good  "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"code","execution_count":9,"id":"20fc1aac","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.060531Z","iopub.status.busy":"2024-12-29T16:39:36.060333Z","iopub.status.idle":"2024-12-29T16:39:36.066682Z","shell.execute_reply":"2024-12-29T16:39:36.066023Z"},"papermill":{"duration":0.016801,"end_time":"2024-12-29T16:39:36.067845","exception":false,"start_time":"2024-12-29T16:39:36.051044","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["pH                              0\n","EC                              0\n","CO3                             0\n","HCO3                            0\n","Cl                              0\n","SO4                             0\n","NO3                             0\n","TH                              0\n","Ca                              0\n","Mg                              0\n","Na                              0\n","K                               0\n","F                               0\n","TDS                             0\n","WQI                             0\n","Water Quality Classification    0\n","dtype: int64"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["df.isnull().sum()"]},{"cell_type":"code","execution_count":10,"id":"62d0443b","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.08748Z","iopub.status.busy":"2024-12-29T16:39:36.087268Z","iopub.status.idle":"2024-12-29T16:39:36.092981Z","shell.execute_reply":"2024-12-29T16:39:36.092353Z"},"papermill":{"duration":0.016626,"end_time":"2024-12-29T16:39:36.094227","exception":false,"start_time":"2024-12-29T16:39:36.077601","status":"completed"},"tags":[]},"outputs":[],"source":["x=df.drop(['WQI','Water Quality Classification'],axis=1)\n","y=df[['WQI','Water Quality Classification']]"]},{"cell_type":"code","execution_count":11,"id":"2444590e","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.112702Z","iopub.status.busy":"2024-12-29T16:39:36.112508Z","iopub.status.idle":"2024-12-29T16:39:36.116587Z","shell.execute_reply":"2024-12-29T16:39:36.115847Z"},"papermill":{"duration":0.014859,"end_time":"2024-12-29T16:39:36.117953","exception":false,"start_time":"2024-12-29T16:39:36.103094","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["((19029, 14), (19029, 2))"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["x.shape,y.shape"]},{"cell_type":"markdown","id":"3f759904","metadata":{"papermill":{"duration":0.009325,"end_time":"2024-12-29T16:39:36.136541","exception":false,"start_time":"2024-12-29T16:39:36.127216","status":"completed"},"tags":[]},"source":["# Splitting and Transforming Data"]},{"cell_type":"code","execution_count":12,"id":"ff82b529","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.156212Z","iopub.status.busy":"2024-12-29T16:39:36.155975Z","iopub.status.idle":"2024-12-29T16:39:36.177783Z","shell.execute_reply":"2024-12-29T16:39:36.177204Z"},"papermill":{"duration":0.03329,"end_time":"2024-12-29T16:39:36.179103","exception":false,"start_time":"2024-12-29T16:39:36.145813","status":"completed"},"tags":[]},"outputs":[],"source":["x_tr,x_val,y_tr,y_val=train_test_split(x,y,test_size=0.3,stratify=y['Water Quality Classification'],random_state=2)"]},{"cell_type":"code","execution_count":13,"id":"b273e1fd","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.198915Z","iopub.status.busy":"2024-12-29T16:39:36.198673Z","iopub.status.idle":"2024-12-29T16:39:36.205116Z","shell.execute_reply":"2024-12-29T16:39:36.204345Z"},"papermill":{"duration":0.017331,"end_time":"2024-12-29T16:39:36.206375","exception":false,"start_time":"2024-12-29T16:39:36.189044","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Water Quality Classification\n","Unsuitable for Drinking    0.347297\n","Poor                       0.279429\n","Very Poor yet Drinkable    0.247447\n","Good                       0.085736\n","Excellent                  0.040090\n","Name: proportion, dtype: float64"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["y_tr['Water Quality Classification'].value_counts(normalize=True)"]},{"cell_type":"code","execution_count":14,"id":"4a3d9a6e","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.225549Z","iopub.status.busy":"2024-12-29T16:39:36.225324Z","iopub.status.idle":"2024-12-29T16:39:36.231413Z","shell.execute_reply":"2024-12-29T16:39:36.230642Z"},"papermill":{"duration":0.016912,"end_time":"2024-12-29T16:39:36.232727","exception":false,"start_time":"2024-12-29T16:39:36.215815","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Water Quality Classification\n","Unsuitable for Drinking    0.347171\n","Poor                       0.279383\n","Very Poor yet Drinkable    0.247504\n","Good                       0.085829\n","Excellent                  0.040112\n","Name: proportion, dtype: float64"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["y_val['Water Quality Classification'].value_counts(normalize=True)"]},{"cell_type":"code","execution_count":15,"id":"43ba47e8","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.252472Z","iopub.status.busy":"2024-12-29T16:39:36.252271Z","iopub.status.idle":"2024-12-29T16:39:36.263236Z","shell.execute_reply":"2024-12-29T16:39:36.262656Z"},"papermill":{"duration":0.021831,"end_time":"2024-12-29T16:39:36.264473","exception":false,"start_time":"2024-12-29T16:39:36.242642","status":"completed"},"tags":[]},"outputs":[],"source":["ss1=StandardScaler()\n","x_tr_ss=ss1.fit_transform(x_tr)\n","x_val_ss=ss1.transform(x_val)"]},{"cell_type":"code","execution_count":16,"id":"ecae14e7","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.283645Z","iopub.status.busy":"2024-12-29T16:39:36.283451Z","iopub.status.idle":"2024-12-29T16:39:36.290403Z","shell.execute_reply":"2024-12-29T16:39:36.289846Z"},"papermill":{"duration":0.017664,"end_time":"2024-12-29T16:39:36.291588","exception":false,"start_time":"2024-12-29T16:39:36.273924","status":"completed"},"tags":[]},"outputs":[],"source":["ss2=StandardScaler()\n","y_tr_reg=ss2.fit_transform(pd.DataFrame(y_tr['WQI']))\n","y_val_reg=ss2.transform(pd.DataFrame(y_val['WQI']))"]},{"cell_type":"code","execution_count":17,"id":"901328d0","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.312271Z","iopub.status.busy":"2024-12-29T16:39:36.312043Z","iopub.status.idle":"2024-12-29T16:39:36.318315Z","shell.execute_reply":"2024-12-29T16:39:36.31769Z"},"papermill":{"duration":0.01771,"end_time":"2024-12-29T16:39:36.319549","exception":false,"start_time":"2024-12-29T16:39:36.301839","status":"completed"},"tags":[]},"outputs":[],"source":["ll=LabelEncoder()\n","y_tr_clf=ll.fit_transform(y_tr['Water Quality Classification'])\n","y_val_clf=ll.transform(y_val['Water Quality Classification'])"]},{"cell_type":"code","execution_count":18,"id":"4a208ca2","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.339221Z","iopub.status.busy":"2024-12-29T16:39:36.338995Z","iopub.status.idle":"2024-12-29T16:39:36.343393Z","shell.execute_reply":"2024-12-29T16:39:36.342506Z"},"papermill":{"duration":0.015815,"end_time":"2024-12-29T16:39:36.345134","exception":false,"start_time":"2024-12-29T16:39:36.329319","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["array(['Excellent', 'Good', 'Poor', 'Unsuitable for Drinking',\n","       'Very Poor yet Drinkable'], dtype=object)"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["ll.classes_"]},{"cell_type":"code","execution_count":19,"id":"fb2c8af8","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.365004Z","iopub.status.busy":"2024-12-29T16:39:36.364801Z","iopub.status.idle":"2024-12-29T16:39:36.369195Z","shell.execute_reply":"2024-12-29T16:39:36.368409Z"},"papermill":{"duration":0.015688,"end_time":"2024-12-29T16:39:36.370349","exception":false,"start_time":"2024-12-29T16:39:36.354661","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["((13320, 14), (13320, 1), (13320,), (5709, 14), (5709, 1), (5709,))"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["x_tr_ss.shape,y_tr_reg.shape,y_tr_clf.shape,x_val_ss.shape,y_val_reg.shape,y_val_clf.shape"]},{"cell_type":"code","execution_count":20,"id":"f57eb970","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.390546Z","iopub.status.busy":"2024-12-29T16:39:36.390348Z","iopub.status.idle":"2024-12-29T16:39:36.416988Z","shell.execute_reply":"2024-12-29T16:39:36.416331Z"},"papermill":{"duration":0.038604,"end_time":"2024-12-29T16:39:36.418298","exception":false,"start_time":"2024-12-29T16:39:36.379694","status":"completed"},"tags":[]},"outputs":[],"source":["X_tr_ss=torch.FloatTensor(x_tr_ss)\n","Y_tr_reg=torch.FloatTensor(y_tr_reg)\n","Y_tr_clf=torch.LongTensor(y_tr_clf)\n","X_val_ss=torch.FloatTensor(x_val_ss)\n","Y_val_reg=torch.FloatTensor(y_val_reg)\n","Y_val_clf=torch.LongTensor(y_val_clf)"]},{"cell_type":"code","execution_count":21,"id":"c9917972","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.438391Z","iopub.status.busy":"2024-12-29T16:39:36.438135Z","iopub.status.idle":"2024-12-29T16:39:36.442606Z","shell.execute_reply":"2024-12-29T16:39:36.441963Z"},"papermill":{"duration":0.015594,"end_time":"2024-12-29T16:39:36.443766","exception":false,"start_time":"2024-12-29T16:39:36.428172","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["(torch.Size([13320, 14]),\n"," torch.Size([13320, 1]),\n"," torch.Size([13320]),\n"," torch.Size([5709, 14]),\n"," torch.Size([5709, 1]),\n"," torch.Size([5709]))"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["X_tr_ss.shape,Y_tr_reg.shape,Y_tr_clf.shape,X_val_ss.shape,Y_val_reg.shape,Y_val_clf.shape"]},{"cell_type":"markdown","id":"96def8f6","metadata":{"papermill":{"duration":0.009617,"end_time":"2024-12-29T16:39:36.462911","exception":false,"start_time":"2024-12-29T16:39:36.453294","status":"completed"},"tags":[]},"source":["# Defining both Architechtures"]},{"cell_type":"code","execution_count":22,"id":"c2ec90ad","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.482483Z","iopub.status.busy":"2024-12-29T16:39:36.482258Z","iopub.status.idle":"2024-12-29T16:39:36.486357Z","shell.execute_reply":"2024-12-29T16:39:36.485707Z"},"papermill":{"duration":0.015249,"end_time":"2024-12-29T16:39:36.487631","exception":false,"start_time":"2024-12-29T16:39:36.472382","status":"completed"},"tags":[]},"outputs":[],"source":["class Net_Reg(nn.Module):\n","    def __init__(self):\n","        super(Net_Reg,self).__init__()\n","        self.linear_layers=Sequential(\n","            Linear(X_tr_ss.shape[1],256),\n","            ReLU(),\n","            Dropout(0.5),\n","            BatchNorm1d(256),\n","            Linear(256,128),\n","            Dropout(0.5),\n","            BatchNorm1d(128),\n","            Linear(128,64),\n","            LeakyReLU(),\n","            Dropout(0.5),\n","            BatchNorm1d(64),\n","            Linear(64,1)\n","        )\n","    def forward(self,x):\n","       x=self.linear_layers(x) \n","       return x \n"]},{"cell_type":"code","execution_count":23,"id":"c0e78d97","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.507685Z","iopub.status.busy":"2024-12-29T16:39:36.507464Z","iopub.status.idle":"2024-12-29T16:39:36.515077Z","shell.execute_reply":"2024-12-29T16:39:36.514357Z"},"papermill":{"duration":0.018984,"end_time":"2024-12-29T16:39:36.516304","exception":false,"start_time":"2024-12-29T16:39:36.49732","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["<torch._C.Generator at 0x7ce12e098910>"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["torch.manual_seed(32)"]},{"cell_type":"code","execution_count":24,"id":"a9505ab2","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.536297Z","iopub.status.busy":"2024-12-29T16:39:36.536035Z","iopub.status.idle":"2024-12-29T16:39:36.561542Z","shell.execute_reply":"2024-12-29T16:39:36.560712Z"},"papermill":{"duration":0.036626,"end_time":"2024-12-29T16:39:36.562744","exception":false,"start_time":"2024-12-29T16:39:36.526118","status":"completed"},"tags":[]},"outputs":[],"source":["model_reg=Net_Reg()"]},{"cell_type":"markdown","id":"10a00dad","metadata":{"papermill":{"duration":0.009955,"end_time":"2024-12-29T16:39:36.582531","exception":false,"start_time":"2024-12-29T16:39:36.572576","status":"completed"},"tags":[]},"source":["Testing if there is any issue with the architechture"]},{"cell_type":"code","execution_count":25,"id":"8956a8e6","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.603089Z","iopub.status.busy":"2024-12-29T16:39:36.602851Z","iopub.status.idle":"2024-12-29T16:39:36.638523Z","shell.execute_reply":"2024-12-29T16:39:36.637722Z"},"papermill":{"duration":0.047364,"end_time":"2024-12-29T16:39:36.639755","exception":false,"start_time":"2024-12-29T16:39:36.592391","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["tensor([ 0.1719,  1.3276, -0.2161,  2.4020,  0.6328,  1.0345,  0.4834,  0.1950,\n","        -0.3418,  0.5472,  1.7343, -0.1679,  0.4191,  0.5206])"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["X_tr_ss[0]"]},{"cell_type":"code","execution_count":26,"id":"2bb86235","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.659893Z","iopub.status.busy":"2024-12-29T16:39:36.659622Z","iopub.status.idle":"2024-12-29T16:39:36.700079Z","shell.execute_reply":"2024-12-29T16:39:36.699179Z"},"papermill":{"duration":0.052065,"end_time":"2024-12-29T16:39:36.701471","exception":false,"start_time":"2024-12-29T16:39:36.649406","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["tensor([[ 0.2584],\n","        [-0.1547]], grad_fn=<AddmmBackward0>)"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["model_reg(X_tr_ss[0:2])"]},{"cell_type":"markdown","id":"fc3c9a19","metadata":{"papermill":{"duration":0.009835,"end_time":"2024-12-29T16:39:36.723055","exception":false,"start_time":"2024-12-29T16:39:36.71322","status":"completed"},"tags":[]},"source":["The architechture is fine"]},{"cell_type":"code","execution_count":27,"id":"3ce598c8","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.744597Z","iopub.status.busy":"2024-12-29T16:39:36.744361Z","iopub.status.idle":"2024-12-29T16:39:36.749133Z","shell.execute_reply":"2024-12-29T16:39:36.748282Z"},"papermill":{"duration":0.017219,"end_time":"2024-12-29T16:39:36.750386","exception":false,"start_time":"2024-12-29T16:39:36.733167","status":"completed"},"tags":[]},"outputs":[],"source":["class Net_Clf(nn.Module):\n","    def __init__(self):\n","        super(Net_Clf,self).__init__()\n","        self.linear_layers=Sequential(\n","            Linear(X_tr_ss.shape[1],256),\n","            ReLU(),\n","            Dropout(0.5),\n","            BatchNorm1d(256),\n","            Linear(256,128),\n","            Dropout(0.5),\n","            BatchNorm1d(128),\n","            Linear(128,64),\n","            LeakyReLU(),\n","            Dropout(0.5),\n","            BatchNorm1d(64),\n","            Linear(64,5),\n","            Softmax(dim=1)\n","        )\n","    def forward(self,x):\n","       x=self.linear_layers(x) \n","       return x \n"]},{"cell_type":"code","execution_count":28,"id":"2a75b409","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.771325Z","iopub.status.busy":"2024-12-29T16:39:36.771102Z","iopub.status.idle":"2024-12-29T16:39:36.776072Z","shell.execute_reply":"2024-12-29T16:39:36.775283Z"},"papermill":{"duration":0.016522,"end_time":"2024-12-29T16:39:36.77721","exception":false,"start_time":"2024-12-29T16:39:36.760688","status":"completed"},"tags":[]},"outputs":[],"source":["model_clf=Net_Clf()"]},{"cell_type":"markdown","id":"34bbd512","metadata":{"papermill":{"duration":0.009659,"end_time":"2024-12-29T16:39:36.796534","exception":false,"start_time":"2024-12-29T16:39:36.786875","status":"completed"},"tags":[]},"source":["Testing if there is any issue with the architechture"]},{"cell_type":"code","execution_count":29,"id":"abc280b6","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.816852Z","iopub.status.busy":"2024-12-29T16:39:36.816598Z","iopub.status.idle":"2024-12-29T16:39:36.821662Z","shell.execute_reply":"2024-12-29T16:39:36.820909Z"},"papermill":{"duration":0.016965,"end_time":"2024-12-29T16:39:36.822994","exception":false,"start_time":"2024-12-29T16:39:36.806029","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["tensor([ 0.1719,  1.3276, -0.2161,  2.4020,  0.6328,  1.0345,  0.4834,  0.1950,\n","        -0.3418,  0.5472,  1.7343, -0.1679,  0.4191,  0.5206])"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["X_tr_ss[0]"]},{"cell_type":"code","execution_count":30,"id":"6a5ab2cc","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.844356Z","iopub.status.busy":"2024-12-29T16:39:36.84412Z","iopub.status.idle":"2024-12-29T16:39:36.855045Z","shell.execute_reply":"2024-12-29T16:39:36.854345Z"},"papermill":{"duration":0.023127,"end_time":"2024-12-29T16:39:36.856264","exception":false,"start_time":"2024-12-29T16:39:36.833137","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["tensor([[0.0913, 0.1351, 0.3905, 0.3079, 0.0753],\n","        [0.3016, 0.2249, 0.0618, 0.0651, 0.3467]], grad_fn=<SoftmaxBackward0>)"]},"execution_count":30,"metadata":{},"output_type":"execute_result"}],"source":["model_clf(X_tr_ss[0:2])"]},{"cell_type":"markdown","id":"60944611","metadata":{"papermill":{"duration":0.00959,"end_time":"2024-12-29T16:39:36.875885","exception":false,"start_time":"2024-12-29T16:39:36.866295","status":"completed"},"tags":[]},"source":["The architechture is fine"]},{"cell_type":"markdown","id":"2bab973f","metadata":{"papermill":{"duration":0.009494,"end_time":"2024-12-29T16:39:36.895072","exception":false,"start_time":"2024-12-29T16:39:36.885578","status":"completed"},"tags":[]},"source":["# Creating Functions"]},{"cell_type":"code","execution_count":31,"id":"bfb58b49","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:36.915163Z","iopub.status.busy":"2024-12-29T16:39:36.914941Z","iopub.status.idle":"2024-12-29T16:39:38.091586Z","shell.execute_reply":"2024-12-29T16:39:38.090895Z"},"papermill":{"duration":1.188434,"end_time":"2024-12-29T16:39:38.093147","exception":false,"start_time":"2024-12-29T16:39:36.904713","status":"completed"},"tags":[]},"outputs":[],"source":["optimizer_reg=Adam(model_reg.parameters(),lr=0.01)\n","optimizer_clf=Adam(model_clf.parameters(),lr=0.01)\n","criteria_clf=CrossEntropyLoss()\n","criteria_reg=MSELoss()"]},{"cell_type":"code","execution_count":32,"id":"c0504591","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:38.114515Z","iopub.status.busy":"2024-12-29T16:39:38.114159Z","iopub.status.idle":"2024-12-29T16:39:38.117366Z","shell.execute_reply":"2024-12-29T16:39:38.116755Z"},"papermill":{"duration":0.014941,"end_time":"2024-12-29T16:39:38.118607","exception":false,"start_time":"2024-12-29T16:39:38.103666","status":"completed"},"tags":[]},"outputs":[],"source":["def clf_f1(y,preds):\n","    return f1_score(y,preds.reshape(-1,1),average='weighted')"]},{"cell_type":"code","execution_count":33,"id":"9a1123d2","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:38.13943Z","iopub.status.busy":"2024-12-29T16:39:38.139217Z","iopub.status.idle":"2024-12-29T16:39:38.142333Z","shell.execute_reply":"2024-12-29T16:39:38.141833Z"},"papermill":{"duration":0.014856,"end_time":"2024-12-29T16:39:38.143453","exception":false,"start_time":"2024-12-29T16:39:38.128597","status":"completed"},"tags":[]},"outputs":[],"source":["def reg_mse(y,preds):\n","    mse=torch.pow(y-preds,2)\n","    return torch.sqrt(torch.mean(mse))"]},{"cell_type":"code","execution_count":34,"id":"4824aaa2","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:38.164811Z","iopub.status.busy":"2024-12-29T16:39:38.164562Z","iopub.status.idle":"2024-12-29T16:39:38.169578Z","shell.execute_reply":"2024-12-29T16:39:38.168734Z"},"papermill":{"duration":0.017239,"end_time":"2024-12-29T16:39:38.170976","exception":false,"start_time":"2024-12-29T16:39:38.153737","status":"completed"},"tags":[]},"outputs":[],"source":["def train_clf(x,y,batch_size=64):\n","    model_clf.train()\n","    train_data=TensorDataset(x,y)\n","    train_loader=DataLoader(train_data,batch_size=batch_size,shuffle=True)\n","    epoch_loss, epoch_acc= 0, 0\n","    no_of_batches = 0\n","    \n","    for x_batch,y_batch in train_loader:\n","        optimizer_clf.zero_grad()\n","        preds=model_clf(x_batch)\n","        corrected_preds=torch.argmax(preds,dim=1)\n","        #print(corrected_preds)\n","        loss=criteria_clf(preds,y_batch)\n","        acc=clf_f1(y_batch,corrected_preds)\n","        loss.backward()\n","        optimizer_clf.step()\n","        epoch_loss+=loss.item()\n","        epoch_acc+=acc.item()\n","        no_of_batches+=1\n","    return epoch_loss/no_of_batches, epoch_acc/no_of_batches   \n","            "]},{"cell_type":"code","execution_count":35,"id":"9fba53c3","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:38.192055Z","iopub.status.busy":"2024-12-29T16:39:38.19182Z","iopub.status.idle":"2024-12-29T16:39:38.19659Z","shell.execute_reply":"2024-12-29T16:39:38.195836Z"},"papermill":{"duration":0.016624,"end_time":"2024-12-29T16:39:38.19783","exception":false,"start_time":"2024-12-29T16:39:38.181206","status":"completed"},"tags":[]},"outputs":[],"source":["def evaluate_clf(x,y,batch_size=64):\n","    model_clf.eval()\n","    train_data=TensorDataset(x,y)\n","    train_loader=DataLoader(train_data,batch_size=batch_size,shuffle=True)\n","    epoch_loss, epoch_acc= 0, 0\n","    no_of_batches = 0\n","    with torch.no_grad():\n","        for x_batch,y_batch in train_loader:\n","            optimizer_clf.zero_grad()\n","            preds=model_clf(x_batch)\n","            corrected_preds=torch.argmax(preds,dim=1)\n","            #print(corrected_preds)\n","            loss=criteria_clf(preds,y_batch)\n","            acc=clf_f1(y_batch,corrected_preds)\n","            epoch_loss+=loss.item()\n","            epoch_acc+=acc.item()\n","            no_of_batches+=1\n","    return epoch_loss/no_of_batches, epoch_acc/no_of_batches   \n","              "]},{"cell_type":"code","execution_count":36,"id":"e548da72","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:38.218475Z","iopub.status.busy":"2024-12-29T16:39:38.218262Z","iopub.status.idle":"2024-12-29T16:39:38.221855Z","shell.execute_reply":"2024-12-29T16:39:38.221212Z"},"papermill":{"duration":0.015335,"end_time":"2024-12-29T16:39:38.223047","exception":false,"start_time":"2024-12-29T16:39:38.207712","status":"completed"},"tags":[]},"outputs":[],"source":["def predict_clf(x):\n","    model_clf.eval()\n","    predictions=[]\n","    preds=model_clf(x)\n","    corrected_preds=torch.argmax(preds,dim=1)\n","    predictions.append(ll.inverse_transform(corrected_preds))\n","    np.concatenate(predictions,axis=0)\n","    return predictions"]},{"cell_type":"code","execution_count":37,"id":"23fc566f","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:38.244283Z","iopub.status.busy":"2024-12-29T16:39:38.24407Z","iopub.status.idle":"2024-12-29T16:39:38.24832Z","shell.execute_reply":"2024-12-29T16:39:38.247659Z"},"papermill":{"duration":0.016095,"end_time":"2024-12-29T16:39:38.249513","exception":false,"start_time":"2024-12-29T16:39:38.233418","status":"completed"},"tags":[]},"outputs":[],"source":["def train_reg(x,y,batch_size=64):\n","    model_reg.train()\n","    train_data=TensorDataset(x,y)\n","    train_loader=DataLoader(train_data,batch_size=batch_size,shuffle=True)\n","    epoch_loss, epoch_acc= 0, 0\n","    no_of_batches = 0\n","    \n","    for x_batch,y_batch in train_loader:\n","        optimizer_reg.zero_grad()\n","        preds=model_reg(x_batch)\n","        loss=criteria_reg(preds,y_batch)\n","        acc=reg_mse(y_batch,preds)\n","        loss.backward()\n","        optimizer_reg.step()\n","        epoch_loss+=loss.item()\n","        epoch_acc+=acc.item()\n","        no_of_batches+=1\n","    return epoch_loss/no_of_batches, epoch_acc/no_of_batches   \n"]},{"cell_type":"code","execution_count":38,"id":"80d1b65e","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:38.270865Z","iopub.status.busy":"2024-12-29T16:39:38.270619Z","iopub.status.idle":"2024-12-29T16:39:38.274972Z","shell.execute_reply":"2024-12-29T16:39:38.274317Z"},"papermill":{"duration":0.016348,"end_time":"2024-12-29T16:39:38.276143","exception":false,"start_time":"2024-12-29T16:39:38.259795","status":"completed"},"tags":[]},"outputs":[],"source":["def evaluate_reg(x,y,batch_size=64):\n","    model_reg.eval()\n","    train_data=TensorDataset(x,y)\n","    train_loader=DataLoader(train_data,batch_size=batch_size,shuffle=True)\n","    epoch_loss, epoch_acc= 0, 0\n","    no_of_batches = 0\n","    with torch.no_grad():\n","        for x_batch,y_batch in train_loader:\n","            optimizer_reg.zero_grad()\n","            preds=model_reg(x_batch)\n","            loss=criteria_reg(preds,y_batch)\n","            acc=reg_mse(y_batch,preds)\n","            epoch_loss+=loss.item()\n","            epoch_acc+=acc.item()\n","            no_of_batches+=1\n","    return epoch_loss/no_of_batches, epoch_acc/no_of_batches   \n","    "]},{"cell_type":"code","execution_count":39,"id":"893205fb","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:38.29639Z","iopub.status.busy":"2024-12-29T16:39:38.29618Z","iopub.status.idle":"2024-12-29T16:39:38.299575Z","shell.execute_reply":"2024-12-29T16:39:38.298994Z"},"papermill":{"duration":0.014731,"end_time":"2024-12-29T16:39:38.300671","exception":false,"start_time":"2024-12-29T16:39:38.28594","status":"completed"},"tags":[]},"outputs":[],"source":["def predict_reg(x):\n","    model_reg.eval()\n","    predictions=[]\n","    preds=model_reg(x)\n","    predictions.append(ss2.inverse_transform(preds.detach().numpy()))\n","    np.concatenate(predictions,axis=0)\n","    return predictions"]},{"cell_type":"markdown","id":"027260db","metadata":{"papermill":{"duration":0.009703,"end_time":"2024-12-29T16:39:38.320572","exception":false,"start_time":"2024-12-29T16:39:38.310869","status":"completed"},"tags":[]},"source":["# Fitting, Evaluating and Loading the best Weights"]},{"cell_type":"code","execution_count":40,"id":"2886f4c6","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:38.341584Z","iopub.status.busy":"2024-12-29T16:39:38.341371Z","iopub.status.idle":"2024-12-29T16:39:38.348203Z","shell.execute_reply":"2024-12-29T16:39:38.347351Z"},"papermill":{"duration":0.019504,"end_time":"2024-12-29T16:39:38.35007","exception":false,"start_time":"2024-12-29T16:39:38.330566","status":"completed"},"tags":[]},"outputs":[],"source":["def run_clf(epochs=50,batch_size=64):\n","    tl=[]\n","    ta=[]\n","    vl=[]\n","    va=[]\n","    best_acc=float(\"-inf\")\n","    for e in range(epochs):\n","        train_loss,train_acc=train_clf(X_tr_ss,Y_tr_clf)\n","        val_loss,val_acc=evaluate_clf(X_val_ss,Y_val_clf)\n","        print(\"\\nEpoch:\",e+1)\n","        print(\"  Train Loss:\",train_loss)\n","        print(\"\\tTrain Acc:\",train_acc)\n","        print(\"\\tValidation Loss:\",val_loss)\n","        print(\"\\tValidation Acc:\",val_acc)\n","        tl.append(round(train_loss,2))\n","        ta.append(round(train_acc,2))\n","        vl.append(round(val_loss,2))\n","        va.append(round(val_acc,2))\n","        if best_acc<=val_acc:\n","            best_acc=val_acc\n","            torch.save(model_clf.state_dict(), 'saved_weights_clf.pt') \n","            print(\"\\n----------------------------------------------------Saved best model------------------------------------------------------------------\")   \n","    #print(tl,ta,vl,va)\n","    plt.plot(range(epochs),tl,label=\"Train Loss\")\n","    plt.plot(range(epochs),vl,label=\"Val Loss\")\n","    plt.title(\"Loss\")\n","    plt.xlabel(\"Epochs\")\n","    plt.ylabel(\"Cross Entropy Loss\")\n","    plt.legend(loc='best')\n","    plt.show()\n","    plt.plot(range(epochs),ta,label=\"Train Acc\")\n","    plt.plot(range(epochs),va,label=\"Val Acc\")\n","    plt.title(\"Accuracy\")\n","    plt.xlabel(\"Epochs\")\n","    plt.ylabel(\"F1 Score\")\n","    plt.legend(loc='best')\n","    plt.show()    "]},{"cell_type":"code","execution_count":41,"id":"e12b4478","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:39:38.371113Z","iopub.status.busy":"2024-12-29T16:39:38.370887Z","iopub.status.idle":"2024-12-29T16:40:46.51167Z","shell.execute_reply":"2024-12-29T16:40:46.510867Z"},"papermill":{"duration":68.153084,"end_time":"2024-12-29T16:40:46.513378","exception":false,"start_time":"2024-12-29T16:39:38.360294","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","Epoch: 1\n","  Train Loss: 1.143618993211591\n","\tTrain Acc: 0.7496126429765045\n","\tValidation Loss: 1.027313862906562\n","\tValidation Acc: 0.8634022627304099\n","\n","----------------------------------------------------Saved best model------------------------------------------------------------------\n","\n","Epoch: 2\n","  Train Loss: 1.0868413861858788\n","\tTrain Acc: 0.8048276657351889\n","\tValidation Loss: 1.0223421348465813\n","\tValidation Acc: 0.8700121803550571\n","\n","----------------------------------------------------Saved best model------------------------------------------------------------------\n","\n","Epoch: 3\n","  Train Loss: 1.0641446284700238\n","\tTrain Acc: 0.8279023519028205\n","\tValidation Loss: 1.011954090330336\n","\tValidation Acc: 0.8759138997322885\n","\n","----------------------------------------------------Saved best model------------------------------------------------------------------\n","\n","Epoch: 4\n","  Train Loss: 1.0655674771829085\n","\tTrain Acc: 0.8261034522824621\n","\tValidation Loss: 1.0586544268661076\n","\tValidation Acc: 0.8354688827822053\n","\n","Epoch: 5\n","  Train Loss: 1.0656307408113799\n","\tTrain Acc: 0.8270129934803527\n","\tValidation Loss: 1.0117502397961087\n","\tValidation Acc: 0.8743523582568806\n","\n","Epoch: 6\n","  Train Loss: 1.0599865151934647\n","\tTrain Acc: 0.8318905047266145\n","\tValidation Loss: 1.0230914956993526\n","\tValidation Acc: 0.864961795028229\n","\n","Epoch: 7\n","  Train Loss: 1.0638410192357295\n","\tTrain Acc: 0.8264370800456443\n","\tValidation Loss: 1.0070185138119592\n","\tValidation Acc: 0.8851080324959081\n","\n","----------------------------------------------------Saved best model------------------------------------------------------------------\n","\n","Epoch: 8\n","  Train Loss: 1.0597903349182822\n","\tTrain Acc: 0.8317207980970451\n","\tValidation Loss: 0.9914260930485196\n","\tValidation Acc: 0.8996857853373594\n","\n","----------------------------------------------------Saved best model------------------------------------------------------------------\n","\n","Epoch: 9\n","  Train Loss: 1.0568250526081433\n","\tTrain Acc: 0.8339649797175807\n","\tValidation Loss: 1.0016253524356418\n","\tValidation Acc: 0.8883380580423701\n","\n","Epoch: 10\n","  Train Loss: 1.0466210807909806\n","\tTrain Acc: 0.8461806783319483\n","\tValidation Loss: 1.0275582273801167\n","\tValidation Acc: 0.8616233423120685\n","\n","Epoch: 11\n","  Train Loss: 1.043816486043793\n","\tTrain Acc: 0.8482355369223081\n","\tValidation Loss: 0.9996627416875628\n","\tValidation Acc: 0.8897322894730968\n","\n","Epoch: 12\n","  Train Loss: 1.0441342817539234\n","\tTrain Acc: 0.8491078788716406\n","\tValidation Loss: 0.9865988307529026\n","\tValidation Acc: 0.9057548744557686\n","\n","----------------------------------------------------Saved best model------------------------------------------------------------------\n","\n","Epoch: 13\n","  Train Loss: 1.0370165354897531\n","\tTrain Acc: 0.8539626736790529\n","\tValidation Loss: 1.0190094159709082\n","\tValidation Acc: 0.871161124942518\n","\n","Epoch: 14\n","  Train Loss: 1.0389055549813229\n","\tTrain Acc: 0.8529102416625491\n","\tValidation Loss: 0.981980197959476\n","\tValidation Acc: 0.9109550119429396\n","\n","----------------------------------------------------Saved best model------------------------------------------------------------------\n","\n","Epoch: 15\n","  Train Loss: 1.0440062558251706\n","\tTrain Acc: 0.8469824072270613\n","\tValidation Loss: 1.0281029615137312\n","\tValidation Acc: 0.8608226092498948\n","\n","Epoch: 16\n","  Train Loss: 1.0469476971329685\n","\tTrain Acc: 0.8449573168504015\n","\tValidation Loss: 1.0387456523047554\n","\tValidation Acc: 0.8450198738962122\n","\n","Epoch: 17\n","  Train Loss: 1.0482251512947265\n","\tTrain Acc: 0.8433008883633686\n","\tValidation Loss: 0.973532087273068\n","\tValidation Acc: 0.9183906319010063\n","\n","----------------------------------------------------Saved best model------------------------------------------------------------------\n","\n","Epoch: 18\n","  Train Loss: 1.0410203950827202\n","\tTrain Acc: 0.8489941811966735\n","\tValidation Loss: 0.989804736773173\n","\tValidation Acc: 0.8990068197346065\n","\n","Epoch: 19\n","  Train Loss: 1.0523291481168646\n","\tTrain Acc: 0.8378124994753675\n","\tValidation Loss: 1.0476037720839182\n","\tValidation Acc: 0.8392504958480891\n","\n","Epoch: 20\n","  Train Loss: 1.0499346954970838\n","\tTrain Acc: 0.8424296664535011\n","\tValidation Loss: 0.997937900490231\n","\tValidation Acc: 0.8909370123963385\n","\n","Epoch: 21\n","  Train Loss: 1.0428943442956111\n","\tTrain Acc: 0.8499855740643025\n","\tValidation Loss: 1.0004162304931217\n","\tValidation Acc: 0.8867809373559696\n","\n","Epoch: 22\n","  Train Loss: 1.0447422020743338\n","\tTrain Acc: 0.845654589242591\n","\tValidation Loss: 0.9867846210797627\n","\tValidation Acc: 0.9026153854682846\n","\n","Epoch: 23\n","  Train Loss: 1.0465259777301807\n","\tTrain Acc: 0.8466555244607442\n","\tValidation Loss: 0.9978152712186178\n","\tValidation Acc: 0.8915054620532904\n","\n","Epoch: 24\n","  Train Loss: 1.0491104026160172\n","\tTrain Acc: 0.843279432110822\n","\tValidation Loss: 0.9863899482621087\n","\tValidation Acc: 0.9020302594432713\n","\n","Epoch: 25\n","  Train Loss: 1.0542901962567746\n","\tTrain Acc: 0.8392361314333949\n","\tValidation Loss: 1.0970634526676601\n","\tValidation Acc: 0.7793993896065577\n","\n","Epoch: 26\n","  Train Loss: 1.056778699302217\n","\tTrain Acc: 0.8359876142176283\n","\tValidation Loss: 0.9763804700639512\n","\tValidation Acc: 0.9151347136819636\n","\n","Epoch: 27\n","  Train Loss: 1.0526181984175906\n","\tTrain Acc: 0.8363812070402327\n","\tValidation Loss: 0.9991876820723216\n","\tValidation Acc: 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Loss: 1.0872337520122528\n","\tValidation Acc: 0.7948180383343004\n","\n","Epoch: 50\n","  Train Loss: 1.0430991940521168\n","\tTrain Acc: 0.8481147670734557\n","\tValidation Loss: 0.9884208930863274\n","\tValidation Acc: 0.9032917503757057\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["run_clf(epochs=50)"]},{"cell_type":"code","execution_count":42,"id":"a4e80d3c","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:40:46.543697Z","iopub.status.busy":"2024-12-29T16:40:46.543473Z","iopub.status.idle":"2024-12-29T16:40:46.552112Z","shell.execute_reply":"2024-12-29T16:40:46.551428Z"},"papermill":{"duration":0.02432,"end_time":"2024-12-29T16:40:46.553397","exception":false,"start_time":"2024-12-29T16:40:46.529077","status":"completed"},"tags":[]},"outputs":[],"source":["def run_reg(epochs=50,batch_size=64):\n","    tl=[]\n","    ta=[]\n","    vl=[]\n","    va=[]\n","    least_error=float(\"inf\")\n","    for e in range(epochs):\n","        train_loss,train_acc=train_reg(X_tr_ss,Y_tr_reg)\n","        val_loss,val_acc=evaluate_reg(X_val_ss,Y_val_reg)\n","        print(\"\\nEpoch:\",e+1)\n","        print(\"  Train Loss:\",train_loss)\n","        print(\"\\tTrain Acc:\",train_acc)\n","        print(\"\\tValidation Loss:\",val_loss)\n","        print(\"\\tValidation Acc:\",val_acc)\n","        tl.append(round(train_loss,2))\n","        ta.append(round(train_acc,2))\n","        vl.append(round(val_loss,2))\n","        va.append(round(val_acc,2))\n","        if least_error>=val_acc:\n","            least_error=val_acc\n","            torch.save(model_reg.state_dict(), 'saved_weights_reg.pt') \n","            print(\"\\n----------------------------------------------------Saved best model------------------------------------------------------------------\")   \n","    #print(tl,ta,vl,va)\n","    plt.plot(range(epochs),tl,label=\"Train Loss\")\n","    plt.plot(range(epochs),vl,label=\"Val Loss\")\n","    plt.title(\"Loss\")\n","    plt.xlabel(\"Epochs\")\n","    plt.ylabel(\"Mean Suared Error Loss\")\n","    plt.legend(loc='best')\n","    plt.show()\n","    plt.plot(range(epochs),ta,label=\"Train Acc\")\n","    plt.plot(range(epochs),va,label=\"Val Acc\")\n","    plt.title(\"Accuracy\")\n","    plt.xlabel(\"Epochs\")\n","    plt.ylabel(\"RMSE\")\n","    plt.legend(loc='best')\n","    plt.show()    "]},{"cell_type":"code","execution_count":43,"id":"30c3d1dc","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:40:46.582742Z","iopub.status.busy":"2024-12-29T16:40:46.582538Z","iopub.status.idle":"2024-12-29T16:41:31.225577Z","shell.execute_reply":"2024-12-29T16:41:31.224849Z"},"papermill":{"duration":44.659916,"end_time":"2024-12-29T16:41:31.227151","exception":false,"start_time":"2024-12-29T16:40:46.567235","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","Epoch: 1\n","  Train Loss: 0.37594826271730747\n","\tTrain Acc: 0.5249177192529423\n","\tValidation Loss: 0.144716130486793\n","\tValidation Acc: 0.29892135469449893\n","\n","----------------------------------------------------Saved best model------------------------------------------------------------------\n","\n","Epoch: 2\n","  Train Loss: 0.31860070504497684\n","\tTrain Acc: 0.4794670678496931\n","\tValidation Loss: 0.1118782712974482\n","\tValidation Acc: 0.23852812722325326\n","\n","----------------------------------------------------Saved best model------------------------------------------------------------------\n","\n","Epoch: 3\n","  Train Loss: 0.3064180160003701\n","\tTrain Acc: 0.4690937566700164\n","\tValidation Loss: 2.0150696508172485\n","\tValidation Acc: 0.7195308132304086\n","\n","Epoch: 4\n","  Train Loss: 0.2977064999809676\n","\tTrain Acc: 0.4549576240150552\n","\tValidation Loss: 0.6572172531858087\n","\tValidation Acc: 0.46497167100509007\n","\n","Epoch: 5\n","  Train Loss: 0.3089248489000295\n","\tTrain Acc: 0.453520429048812\n","\tValidation Loss: 2.916577168429891\n","\tValidation Acc: 0.916074456108941\n","\n","Epoch: 6\n","  Train Loss: 0.2779966222042102\n","\tTrain Acc: 0.4459093128237428\n","\tValidation Loss: 1.0476650828961283\n","\tValidation Acc: 0.5695562199585967\n","\n","Epoch: 7\n","  Train Loss: 0.28346180340105837\n","\tTrain Acc: 0.4496112619860891\n","\tValidation Loss: 0.7083630862542325\n","\tValidation Acc: 0.44126432173781927\n","\n","Epoch: 8\n","  Train Loss: 0.2910948405354217\n","\tTrain Acc: 0.4675023081627759\n","\tValidation Loss: 4.2363163829574155\n","\tValidation Acc: 1.1697913004292382\n","\n","Epoch: 9\n","  Train Loss: 0.27463371780785645\n","\tTrain Acc: 0.4459070468490774\n","\tValidation Loss: 1.2777755396906287\n","\tValidation Acc: 0.5524421477483379\n","\n","Epoch: 10\n","  Train Loss: 0.2780697905418405\n","\tTrain Acc: 0.44930198795772625\n","\tValidation Loss: 1.3984673446561726\n","\tValidation Acc: 0.489492224322425\n","\n","Epoch: 11\n","  Train Loss: 0.2690670193756739\n","\tTrain Acc: 0.43691730299634796\n","\tValidation Loss: 1.8096073754959636\n","\tValidation Acc: 0.7082134455442428\n","\n","Epoch: 12\n","  Train Loss: 0.26709066131753784\n","\tTrain Acc: 0.4438700499146749\n","\tValidation Loss: 1.171772908936772\n","\tValidation Acc: 0.5277198776602745\n","\n","Epoch: 13\n","  Train Loss: 0.2642407404998558\n","\tTrain Acc: 0.43185389034770894\n","\tValidation Loss: 1.9729446155433026\n","\tValidation Acc: 0.7255583906339275\n","\n","Epoch: 14\n","  Train Loss: 0.2499959205575822\n","\tTrain Acc: 0.43031148726575114\n","\tValidation Loss: 2.513038678529362\n","\tValidation Acc: 0.8038782172732883\n","\n","Epoch: 15\n","  Train Loss: 0.25729073831838284\n","\tTrain Acc: 0.4323573815480374\n","\tValidation Loss: 2.8598353470882607\n","\tValidation Acc: 0.8868730627828174\n","\n","Epoch: 16\n","  Train Loss: 0.2542303539099591\n","\tTrain Acc: 0.43413540364452524\n","\tValidation Loss: 0.7168335115744008\n","\tValidation Acc: 0.46793353342347677\n","\n","Epoch: 17\n","  Train Loss: 0.28494418540782335\n","\tTrain Acc: 0.4575443149468545\n","\tValidation Loss: 5.227992780889488\n","\tValidation Acc: 0.8779757066733307\n","\n","Epoch: 18\n","  Train Loss: 0.25959807170920396\n","\tTrain Acc: 0.43917852861173984\n","\tValidation Loss: 4.270744507956422\n","\tValidation Acc: 0.8740677990847163\n","\n","Epoch: 19\n","  Train Loss: 0.285637589732996\n","\tTrain Acc: 0.4516370950988605\n","\tValidation Loss: 3.010229512904253\n","\tValidation Acc: 0.8380728494789865\n","\n","Epoch: 20\n","  Train Loss: 0.26204796573904715\n","\tTrain Acc: 0.4451206832696376\n","\tValidation Loss: 3.8067419953644275\n","\tValidation Acc: 0.9797672374380959\n","\n","Epoch: 21\n","  Train Loss: 0.23771042966742836\n","\tTrain Acc: 0.42615408539486843\n","\tValidation Loss: 3.0932358520105483\n","\tValidation Acc: 0.8832267352276378\n","\n","Epoch: 22\n","  Train Loss: 0.2404175831905107\n","\tTrain Acc: 0.43634930138952993\n","\tValidation Loss: 1.3526919478550554\n","\tValidation Acc: 0.5870099941889445\n","\n","Epoch: 23\n","  Train Loss: 0.27076089849169743\n","\tTrain Acc: 0.44888203309483504\n","\tValidation Loss: 1.4611266711519824\n","\tValidation Acc: 0.5987646927436193\n","\n","Epoch: 24\n","  Train Loss: 0.26255171975949737\n","\tTrain Acc: 0.4401964773401689\n","\tValidation Loss: 1.4072381170673502\n","\tValidation Acc: 0.6458346666561232\n","\n","Epoch: 25\n","  Train Loss: 0.24633760844762817\n","\tTrain Acc: 0.4342149686442608\n","\tValidation Loss: 5.755778934149485\n","\tValidation Acc: 1.0110545587208537\n","\n","Epoch: 26\n","  Train Loss: 0.2485705454205497\n","\tTrain Acc: 0.43426008094726\n","\tValidation Loss: 5.297006596128146\n","\tValidation Acc: 0.9943367873628934\n","\n","Epoch: 27\n","  Train Loss: 0.2769076053463101\n","\tTrain Acc: 0.44677206688520443\n","\tValidation Loss: 1.1472171648922893\n","\tValidation Acc: 0.5976370039913389\n","\n","Epoch: 28\n","  Train Loss: 0.2426960214116927\n","\tTrain Acc: 0.430025750726604\n","\tValidation Loss: 1.394163464785864\n","\tValidation Acc: 0.6378357782132096\n","\n","Epoch: 29\n","  Train Loss: 0.2535836319639637\n","\tTrain Acc: 0.4391628797544817\n","\tValidation Loss: 2.7935269373779494\n","\tValidation Acc: 0.7816751225127114\n","\n","Epoch: 30\n","  Train Loss: 0.24977986369299832\n","\tTrain Acc: 0.44130174558984037\n","\tValidation Loss: 5.133517213579681\n","\tValidation Acc: 1.1139281377196313\n","\n","Epoch: 31\n","  Train Loss: 0.25708023802729313\n","\tTrain Acc: 0.4396625840635391\n","\tValidation Loss: 3.7545703635861476\n","\tValidation Acc: 0.9735366718636619\n","\n","Epoch: 32\n","  Train Loss: 0.2481868864353479\n","\tTrain Acc: 0.4385532631258075\n","\tValidation Loss: 4.572329516626066\n","\tValidation Acc: 1.0452939654390017\n","\n","Epoch: 33\n","  Train Loss: 0.25939881534239895\n","\tTrain Acc: 0.4226946994020608\n","\tValidation Loss: 2.003013941794375\n","\tValidation Acc: 0.7583682822477487\n","\n","Epoch: 34\n","  Train Loss: 0.2577161689837013\n","\tTrain Acc: 0.426564726105147\n","\tValidation Loss: 2.1633479491290117\n","\tValidation Acc: 0.7733891647722986\n","\n","Epoch: 35\n","  Train Loss: 0.25090042596157086\n","\tTrain Acc: 0.4258797764778137\n","\tValidation Loss: 0.800215331154565\n","\tValidation Acc: 0.4912214628524251\n","\n","Epoch: 36\n","  Train Loss: 0.25074145935796666\n","\tTrain Acc: 0.4363854032669341\n","\tValidation Loss: 5.418570706124107\n","\tValidation Acc: 1.098558699256844\n","\n","Epoch: 37\n","  Train Loss: 0.2378470941522475\n","\tTrain Acc: 0.4251683678068042\n","\tValidation Loss: 2.123726007590691\n","\tValidation Acc: 0.7439279481768608\n","\n","Epoch: 38\n","  Train Loss: 0.2640529751029026\n","\tTrain Acc: 0.43314070742951627\n","\tValidation Loss: 1.8215887936866946\n","\tValidation Acc: 0.6704240375094943\n","\n","Epoch: 39\n","  Train Loss: 0.2571819910877629\n","\tTrain Acc: 0.4465362279181275\n","\tValidation Loss: 5.828707260100378\n","\tValidation Acc: 1.2416205394599173\n","\n","Epoch: 40\n","  Train Loss: 0.2548353371152467\n","\tTrain Acc: 0.43467848772922774\n","\tValidation Loss: 1.2580166585950388\n","\tValidation Acc: 0.5696927310691939\n","\n","Epoch: 41\n","  Train Loss: 0.2555631112895514\n","\tTrain Acc: 0.43556212295185437\n","\tValidation Loss: 7.866362435774257\n","\tValidation Acc: 1.0827048253681926\n","\n","Epoch: 42\n","  Train Loss: 0.24731352665065007\n","\tTrain Acc: 0.43229375344714477\n","\tValidation Loss: 6.241570443225403\n","\tValidation Acc: 1.0826722151703305\n","\n","Epoch: 43\n","  Train Loss: 0.24436257444500353\n","\tTrain Acc: 0.4243901796175532\n","\tValidation Loss: 3.0645353565199507\n","\tValidation Acc: 0.8486534537540542\n","\n","Epoch: 44\n","  Train Loss: 0.24961832666582467\n","\tTrain Acc: 0.4361829344878356\n","\tValidation Loss: 2.2163088586595325\n","\tValidation Acc: 0.7390710410144594\n","\n","Epoch: 45\n","  Train Loss: 0.2599117712398465\n","\tTrain Acc: 0.4390361991367842\n","\tValidation Loss: 0.876307547547751\n","\tValidation Acc: 0.510446947813034\n","\n","Epoch: 46\n","  Train Loss: 0.2432431227139879\n","\tTrain Acc: 0.42731849343012396\n","\tValidation Loss: 0.8680082056671381\n","\tValidation Acc: 0.5383455637428496\n","\n","Epoch: 47\n","  Train Loss: 0.26017103486796883\n","\tTrain Acc: 0.44429356237632806\n","\tValidation Loss: 1.6701948846379915\n","\tValidation Acc: 0.7114819160766072\n","\n","Epoch: 48\n","  Train Loss: 0.23769563176272587\n","\tTrain Acc: 0.41730264941471046\n","\tValidation Loss: 2.364189229781429\n","\tValidation Acc: 0.7855396764145957\n","\n","Epoch: 49\n","  Train Loss: 0.24189639646720373\n","\tTrain Acc: 0.42583626330469215\n","\tValidation Loss: 3.0564432174588245\n","\tValidation Acc: 0.8364142798715167\n","\n","Epoch: 50\n","  Train Loss: 0.25316208305113624\n","\tTrain Acc: 0.4264739152917451\n","\tValidation Loss: 1.0949957326882416\n","\tValidation Acc: 0.549646822280354\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["run_reg(epochs=50)"]},{"cell_type":"code","execution_count":44,"id":"4eedb967","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:41:31.267342Z","iopub.status.busy":"2024-12-29T16:41:31.267071Z","iopub.status.idle":"2024-12-29T16:41:31.307035Z","shell.execute_reply":"2024-12-29T16:41:31.306014Z"},"papermill":{"duration":0.061926,"end_time":"2024-12-29T16:41:31.308738","exception":false,"start_time":"2024-12-29T16:41:31.246812","status":"completed"},"tags":[]},"outputs":[],"source":["model_clf.load_state_dict(torch.load('saved_weights_clf.pt'))\n","predictions_clf=predict_clf(X_val_ss)\n","model_reg.load_state_dict(torch.load('saved_weights_reg.pt'))\n","predictions_reg=predict_reg(X_val_ss)"]},{"cell_type":"code","execution_count":45,"id":"d14e91f1","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:41:31.352459Z","iopub.status.busy":"2024-12-29T16:41:31.352173Z","iopub.status.idle":"2024-12-29T16:41:31.357276Z","shell.execute_reply":"2024-12-29T16:41:31.356553Z"},"papermill":{"duration":0.027571,"end_time":"2024-12-29T16:41:31.358588","exception":false,"start_time":"2024-12-29T16:41:31.331017","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["[array(['Poor', 'Good', 'Good', ..., 'Unsuitable for Drinking', 'Poor',\n","        'Unsuitable for Drinking'], dtype=object)]"]},"execution_count":45,"metadata":{},"output_type":"execute_result"}],"source":["predictions_clf"]},{"cell_type":"code","execution_count":46,"id":"90be876d","metadata":{"execution":{"iopub.execute_input":"2024-12-29T16:41:31.396496Z","iopub.status.busy":"2024-12-29T16:41:31.396273Z","iopub.status.idle":"2024-12-29T16:41:31.401154Z","shell.execute_reply":"2024-12-29T16:41:31.400316Z"},"papermill":{"duration":0.025546,"end_time":"2024-12-29T16:41:31.402678","exception":false,"start_time":"2024-12-29T16:41:31.377132","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["[array([[143.01414],\n","        [125.19445],\n","        [ 85.03072],\n","        ...,\n","        [331.88672],\n","        [161.35457],\n","        [317.36984]], dtype=float32)]"]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["predictions_reg"]},{"cell_type":"code","execution_count":null,"id":"6df2c350","metadata":{"papermill":{"duration":0.018634,"end_time":"2024-12-29T16:41:31.440964","exception":false,"start_time":"2024-12-29T16:41:31.42233","status":"completed"},"tags":[]},"outputs":[],"source":[]}],"metadata":{"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"datasetId":6394623,"sourceId":10327594,"sourceType":"datasetVersion"}],"dockerImageVersionId":30823,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","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":"3.10.12"},"papermill":{"default_parameters":{},"duration":125.76736,"end_time":"2024-12-29T16:41:33.80058","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2024-12-29T16:39:28.03322","version":"2.6.0"}},"nbformat":4,"nbformat_minor":5}