80 KiB
80 KiB
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
import xgboost as xgb
import torch
def eval(model,x_test,y_test, nn = None):
if nn == None:
print(classification_report(y_test,model.predict(x_test)))
else:
y_pred = model(torch.tensor(x_test.astype(np.float32)))
y_pred = y_pred.cpu().detach().numpy()
y_pred = (y_pred > 0.5)
y_pred = np.asarray(y_pred, dtype=np.int32)
print(classification_report(y_test,y_pred))
data = pd.read_csv('water_potability.csv')data.describe()| ph | Hardness | Solids | Chloramines | Sulfate | Conductivity | Organic_carbon | Trihalomethanes | Turbidity | Potability | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 2785.000000 | 3276.000000 | 3276.000000 | 3276.000000 | 2495.000000 | 3276.000000 | 3276.000000 | 3114.000000 | 3276.000000 | 3276.000000 |
| mean | 7.080795 | 196.369496 | 22014.092526 | 7.122277 | 333.775777 | 426.205111 | 14.284970 | 66.396293 | 3.966786 | 0.390110 |
| std | 1.594320 | 32.879761 | 8768.570828 | 1.583085 | 41.416840 | 80.824064 | 3.308162 | 16.175008 | 0.780382 | 0.487849 |
| min | 0.000000 | 47.432000 | 320.942611 | 0.352000 | 129.000000 | 181.483754 | 2.200000 | 0.738000 | 1.450000 | 0.000000 |
| 25% | 6.093092 | 176.850538 | 15666.690297 | 6.127421 | 307.699498 | 365.734414 | 12.065801 | 55.844536 | 3.439711 | 0.000000 |
| 50% | 7.036752 | 196.967627 | 20927.833607 | 7.130299 | 333.073546 | 421.884968 | 14.218338 | 66.622485 | 3.955028 | 0.000000 |
| 75% | 8.062066 | 216.667456 | 27332.762127 | 8.114887 | 359.950170 | 481.792304 | 16.557652 | 77.337473 | 4.500320 | 1.000000 |
| max | 14.000000 | 323.124000 | 61227.196008 | 13.127000 | 481.030642 | 753.342620 | 28.300000 | 124.000000 | 6.739000 | 1.000000 |
data = data.dropna()plt.figure(figsize=(20,17))
matrix = np.triu(data.corr())
sns.heatmap(data.corr(), annot=True, linewidth=0, mask=matrix)<AxesSubplot:>
data_feat = data.iloc[:, :-1]
data_target = data.iloc[:, -1]
st = StandardScaler()
data_feat = st.fit_transform(data_feat)X_train, X_test, Y_train, Y_test = train_test_split(data_feat, data_target, random_state = 42, test_size = 0.1)DT = DecisionTreeClassifier(criterion = 'gini').fit(X_train, Y_train)eval(DT,X_test,Y_test) precision recall f1-score support
0 0.72 0.67 0.69 123
1 0.53 0.59 0.56 79
accuracy 0.64 202
macro avg 0.63 0.63 0.63 202
weighted avg 0.65 0.64 0.64 202
SV = SVC(gamma='scale').fit(X_train, Y_train)eval(SV,X_test,Y_test) precision recall f1-score support
0 0.70 0.93 0.80 123
1 0.78 0.39 0.52 79
accuracy 0.72 202
macro avg 0.74 0.66 0.66 202
weighted avg 0.73 0.72 0.69 202
import xgboost as xgb
xgb_model = xgb.XGBClassifier(objective="binary:logistic",
eval_metric='logloss',
use_label_encoder = False,
booster='gbtree',reg_lambda=3).fit(X_train, Y_train)eval(xgb_model,X_test,Y_test) precision recall f1-score support
0 0.67 0.72 0.69 123
1 0.50 0.44 0.47 79
accuracy 0.61 202
macro avg 0.58 0.58 0.58 202
weighted avg 0.60 0.61 0.60 202
kn = KNeighborsClassifier(n_neighbors=1,
algorithm='kd_tree').fit(X_train, Y_train)eval(kn,X_test,Y_test) precision recall f1-score support
0 0.67 0.71 0.69 123
1 0.50 0.46 0.48 79
accuracy 0.61 202
macro avg 0.58 0.58 0.58 202
weighted avg 0.60 0.61 0.61 202
class NeuralNetworkModel(torch.nn.Module):
def __init__(self):
super(NeuralNetworkModel, self).__init__()
self.fc1 = torch.nn.Linear(9,2000)
self.fc2 = torch.nn.Linear(2000,1000)
self.fc3 = torch.nn.Linear(1000,1)
def forward(self, x):
x = self.fc1(x)
x = torch.relu(x)
x = self.fc2(x)
x = torch.relu(x)
x = self.fc3(x)
x = torch.sigmoid(x)
return x
model_nn = NeuralNetworkModel()
criterion = torch.nn.BCELoss()
optimizer = torch.optim.SGD(model_nn.parameters(), lr=0.01)
batch_size = 3
for epoch in range(6):
loss_score = 0
acc_score = 0
items_total = 0
model_nn.train()
for i in range(0, Y_train.to_numpy().shape[0], batch_size):
X = X_train[i:i+batch_size]
X = torch.tensor(X.astype(np.float32))
Y = Y_train[i:i+batch_size].to_numpy()
Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)
Y_predictions = model_nn(X)
acc_score += torch.sum((Y_predictions > 0.5) == Y).item()
items_total += Y.shape[0]
optimizer.zero_grad()
loss = criterion(Y_predictions, Y)
loss.backward()
optimizer.step()
loss_score += loss.item() * Y.shape[0]eval(model_nn,X_test,Y_test, 1) precision recall f1-score support
0 0.70 0.89 0.78 123
1 0.70 0.41 0.51 79
accuracy 0.70 202
macro avg 0.70 0.65 0.65 202
weighted avg 0.70 0.70 0.68 202
class LogisticRegressionModel(torch.nn.Module):
def __init__(self):
super(LogisticRegressionModel, self).__init__()
self.fc = torch.nn.Linear(9,1)
def forward(self, x):
x = self.fc(x)
x = torch.sigmoid(x)
return x
lr_model = LogisticRegressionModel()
BATCH_SIZE = 2
criterion = torch.nn.BCELoss()
optimizer = torch.optim.Adam(lr_model.parameters(), lr = 0.1)
loss_score = 0
acc_score = 0
items_total = 0
lr_model.train()
for i in range(0, Y_train.shape[0], BATCH_SIZE):
X = X_train[i:i+BATCH_SIZE]
X = torch.tensor(X.astype(np.float32))
Y = Y_train[i:i+BATCH_SIZE].to_numpy()
Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)
Y_predictions = lr_model(X)
acc_score += torch.sum((Y_predictions > 0.5) == Y).item()
items_total += Y.shape[0]
optimizer.zero_grad()
loss = criterion(Y_predictions, Y)
loss.backward()
optimizer.step()
loss_score += loss.item() * Y.shape[0] eval(lr_model,X_test,Y_test, 1) precision recall f1-score support
0 0.63 0.80 0.71 123
1 0.47 0.28 0.35 79
accuracy 0.59 202
macro avg 0.55 0.54 0.53 202
weighted avg 0.57 0.59 0.57 202