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July 20, 2022 05:16
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| import torch | |
| import pandas as pd | |
| from sklearn.preprocessing import LabelEncoder | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.metrics import accuracy_score,f1_score | |
| from pymlpipe.tabular import PyMLPipe | |
| df=pd.read_csv("train.csv") | |
| encoders=["area_code","state","international_plan","voice_mail_plan","churn"] | |
| for i in encoders: | |
| le=LabelEncoder() | |
| df[i]=le.fit_transform(df[i]) | |
| trainy=df["churn"] | |
| trainx=df[['state', 'account_length', 'area_code', 'international_plan', | |
| 'voice_mail_plan', 'number_vmail_messages', 'total_day_minutes', | |
| 'total_day_calls', 'total_day_charge', 'total_eve_minutes', | |
| 'total_eve_calls', 'total_eve_charge', 'total_night_minutes', | |
| 'total_night_calls', 'total_night_charge', 'total_intl_minutes', | |
| 'total_intl_calls', 'total_intl_charge', | |
| 'number_customer_service_calls']] | |
| class Model(torch.nn.Module): | |
| def __init__(self,col_size): | |
| super().__init__() | |
| # using sequencial | |
| self.seq=torch.nn.Sequential( | |
| torch.nn.Linear(col_size,15), | |
| torch.nn.ReLU(), | |
| torch.nn.Linear(15,10), | |
| torch.nn.ReLU(), | |
| torch.nn.Linear(10,1) | |
| ) | |
| #using torch layers | |
| ''' | |
| self.linear_layer_1=torch.nn.Linear(col_size,15) | |
| self.relu_1=torch.nn.ReLU() | |
| self.linear_layer_2=torch.nn.Linear(15,10) | |
| self.relu_2=torch.nn.ReLU() | |
| self.linear_layer_3=torch.nn.Linear(10,1) | |
| ''' | |
| def forward(self,x): | |
| out=self.seq(x) | |
| ''' | |
| out=self.relu_1(self.linear_layer_1(x)) | |
| out=self.relu_12self.linear_layer_3(out)) | |
| out=self.linear_layer_3(out) | |
| ''' | |
| return torch.sigmoid(out) | |
| model=Model(len(trainx.columns)) | |
| train_x,test_x,train_y,test_y=train_test_split(trainx,trainy) | |
| train_x=torch.from_numpy(train_x.values) | |
| train_x=train_x.type(torch.FloatTensor) | |
| train_y=torch.from_numpy(train_y.values) | |
| train_y=train_y.type(torch.FloatTensor) | |
| test_x=torch.from_numpy(test_x.values) | |
| test_x=test_x.type(torch.FloatTensor) | |
| test_y=torch.from_numpy(test_y.values) | |
| test_y=test_y.type(torch.FloatTensor) | |
| optimizer=torch.optim.SGD(model.parameters(),lr=0.001) | |
| criterion=torch.nn.BCELoss() | |
| def validate(model,testx,testy): | |
| prediction=model(testx) | |
| prediction=torch.where(prediction>.5,1,0) | |
| accu=accuracy_score(prediction.detach().numpy(),test_y.unsqueeze(1).detach().numpy()) | |
| f1=f1_score(prediction.detach().numpy(),test_y.unsqueeze(1).detach().numpy()) | |
| return {"accuracy":accu,"f1":f1} | |
| epochs=100 | |
| batch_size=1000 | |
| mlp=PyMLPipe() | |
| mlp.set_experiment("Pytorch") | |
| mlp.set_version(0.2) | |
| with mlp.run(): | |
| mlp.register_artifact("churndata.csv",df) | |
| mlp.log_params({ | |
| "lr":0.01, | |
| "optimizer":"SGD", | |
| "loss_fuction":"BCEloss" | |
| }) | |
| for epoch in range(epochs): | |
| loss_batch=0 | |
| for batch in range(1000,5000,1000): | |
| optimizer.zero_grad() | |
| train_data=train_x[batch-1000:batch] | |
| output=model(train_data) | |
| loss=criterion(output,train_y[batch-1000:batch].unsqueeze(1)) | |
| loss.backward() | |
| optimizer.step() | |
| loss_batch+=loss.item() | |
| metrics=validate(model,test_x,test_y) | |
| metrics["loss"]=loss_batch | |
| metrics["epoch"]=epoch | |
| mlp.log_metrics_continious(metrics) | |
| mlp.pytorch.register_model("pytorch_example1", model) | |
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