From 03a8b1321ef956dbc77873f6921e967a8ef0dd4f Mon Sep 17 00:00:00 2001 From: Natalia Szymczyk Date: Fri, 30 Jun 2023 17:22:42 +0200 Subject: [PATCH] lab05, lab07 --- Dockerfile | 6 +- dev.data | 94 +++++ model_scripted.pt | Bin 0 -> 170637 bytes my_runs/1/config.json | 6 + my_runs/1/cout.txt | 24 ++ my_runs/1/metrics.json | 1 + my_runs/1/run.json | 71 ++++ .../train_f31ea0fb211226b21997ea6ed8f8699c.py | 97 +++++ predict.py | 61 +++ test.data | 116 ++++++ train.data | 372 ++++++++++++++++++ train.py | 99 +++++ 12 files changed, 945 insertions(+), 2 deletions(-) create mode 100644 dev.data create mode 100644 model_scripted.pt create mode 100644 my_runs/1/config.json create mode 100644 my_runs/1/cout.txt create mode 100644 my_runs/1/metrics.json create mode 100644 my_runs/1/run.json create mode 100644 my_runs/_sources/train_f31ea0fb211226b21997ea6ed8f8699c.py create mode 100644 predict.py create mode 100644 test.data create mode 100644 train.data create mode 100644 train.py diff --git a/Dockerfile b/Dockerfile index 280aedc..49f95bc 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,5 +1,7 @@ FROM ubuntu:latest -RUN apt update && apt install -y python3-pip && pip3 install scikit-learn datasets +RUN apt update && apt install -y python3-pip && pip3 install scikit-learn datasets torch pandas -ENV HF_HOME="/tmp/hf" \ No newline at end of file +ENV HF_HOME="/tmp/hf" + +COPY . . \ No newline at end of file diff --git a/dev.data b/dev.data new file mode 100644 index 0000000..1e40bfe --- /dev/null +++ b/dev.data @@ -0,0 +1,94 @@ +,age,is_male,total_bilirubin,direct_ribilubin,alkaline_phosphotase,alamine_aminotransferasi,aspartate_aminotransferase,total_proteins,albumin,albumin_to_globulin_ratio,class +582,0.40322580645161293,True,0.01809954751131222,0.01709401709401709,0.0691747572815534,0.005527638190954774,0.00408997955010225,0.7254901960784315,0.7317073170731709,0.8461538461538465,1 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+INFO - sacred_scopes - Started run with ID "1" +ClassificationModel( + (layer_1): Linear(in_features=10, out_features=256, bias=True) + (layer_2): Linear(in_features=256, out_features=128, bias=True) + (layer_out): Linear(in_features=128, out_features=1, bias=True) + (relu): ReLU() + (sigmoid): Sigmoid() + (dropout): Dropout(p=0.1, inplace=False) + (batchnorm1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (batchnorm2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) +) +Loss in 0. iteration: 0.773659348487854 +Loss in 10. iteration: 0.6972044110298157 +Loss in 20. iteration: 0.6682128310203552 +Loss in 30. iteration: 0.6499606966972351 +Loss in 40. iteration: 0.6151337027549744 +Loss in 50. iteration: 0.6123079657554626 +Loss in 60. iteration: 0.6080400347709656 +Loss in 70. iteration: 0.6123043298721313 +Loss in 80. iteration: 0.5966068506240845 +Loss in 90. iteration: 0.5919382572174072 +Last iteration loss value: 0.5908359885215759 +INFO - sacred_scopes - Completed after 0:00:03 diff --git a/my_runs/1/metrics.json b/my_runs/1/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/my_runs/1/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_runs/1/run.json b/my_runs/1/run.json new file mode 100644 index 0000000..598339c --- /dev/null +++ b/my_runs/1/run.json @@ -0,0 +1,71 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "/Users/natalia.szymczyk/Workspace/untitled folder/uam/ium/ium_z487186", + "dependencies": [ + "numpy==1.24.3", + "pandas==1.5.3", + "sacred==0.8.4", + "scikit-learn==1.2.2", + "torch==2.0.0" + ], + "mainfile": "train.py", + "name": "sacred_scopes", + "repositories": [ + { + "commit": "d9b911b767241c4d230c6fcd6199e6639d8b8966", + "dirty": true, + "url": "https://git.wmi.amu.edu.pl/s487186/ium_z487186.git" + } + ], + "sources": [ + [ + "train.py", + "_sources/train_f31ea0fb211226b21997ea6ed8f8699c.py" + ] + ] + }, + "heartbeat": "2023-06-30T15:18:34.885910", + "host": { + "ENV": {}, + "cpu": "Apple M1 Pro", + "hostname": "polpc11496", + "os": [ + "Darwin", + "macOS-13.4-arm64-arm-64bit" + ], + "python_version": "3.11.4" + }, + "meta": { + "command": "my_main", + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false + } + }, + "resources": [], + "result": null, + "start_time": "2023-06-30T15:18:31.480266", + "status": "COMPLETED", + "stop_time": "2023-06-30T15:18:34.883899" +} \ No newline at end of file diff --git a/my_runs/_sources/train_f31ea0fb211226b21997ea6ed8f8699c.py b/my_runs/_sources/train_f31ea0fb211226b21997ea6ed8f8699c.py new file mode 100644 index 0000000..30f1cfe --- /dev/null +++ b/my_runs/_sources/train_f31ea0fb211226b21997ea6ed8f8699c.py @@ -0,0 +1,97 @@ +import pandas as pd +import torch +import torch.nn as nn + +from sklearn.preprocessing import MinMaxScaler +from torch.utils.data import TensorDataset +from torch.utils.data import DataLoader + +from sacred import Experiment +from sacred.observers import FileStorageObserver + +exint = Experiment("sacred_scopes", interactive=True) +exint.observers.append(FileStorageObserver('my_runs')) + +@exint.config +def my_config(): + batch_size = 64 + learning_rate = 0.001 + epochs = 100 + + +@exint.capture +def prepare_message(msg): + return msg + + +@exint.main +def my_main(batch_size, learning_rate, epochs): + train_file = pd.read_csv('train.data').drop('Unnamed: 0', axis=1) + df_pandas = train_file.dropna() + + X_train = df_pandas.drop('class', axis=1) + Y_train = df_pandas['class'] + + scaler = MinMaxScaler() + X_train = scaler.fit_transform(X_train) + + x_tensor = torch.tensor(X_train).float() + y_tensor = torch.tensor(Y_train.values).float() + + train_ds = TensorDataset(x_tensor, y_tensor.unsqueeze(1)) + train_dl = DataLoader(train_ds, batch_size=batch_size) + + class ClassificationModel(nn.Module): + def __init__(self, n_input_dim): + super(ClassificationModel, self).__init__() + self.layer_1 = nn.Linear(n_input_dim, 256) + self.layer_2 = nn.Linear(256, 128) + self.layer_out = nn.Linear(128, 1) + + self.relu = nn.ReLU() + self.sigmoid = nn.Sigmoid() + self.dropout = nn.Dropout(p=0.1) + self.batchnorm1 = nn.BatchNorm1d(256) + self.batchnorm2 = nn.BatchNorm1d(128) + + def forward(self, inputs): + x = self.relu(self.layer_1(inputs)) + x = self.batchnorm1(x) + x = self.relu(self.layer_2(x)) + x = self.batchnorm2(x) + x = self.dropout(x) + x = self.sigmoid(self.layer_out(x)) + + return x + + + model = ClassificationModel(X_train.shape[1]) + print(model) + + + loss_func = nn.BCEWithLogitsLoss() + optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) + + model.train() + train_loss = [] + + for epoch in range(epochs): + for xb, yb in train_dl: + y_pred = model(xb) # Forward Propagation + loss = loss_func(y_pred, yb) # Loss Computation + optimizer.zero_grad() # Clearing all previous gradients, setting to zero + loss.backward() # Back Propagation + optimizer.step() # Updating the parameters + + if epoch % 10 == 0: + print(f"Loss in {epoch}. iteration: {loss.item()}") + train_loss.append(loss.item()) + print('Last iteration loss value: '+str(loss.item())) + + + model_scripted = torch.jit.script(model) # Export to TorchScript + model_scripted.save('model_scripted.pt') # Save + + +exint.run() + diff --git a/predict.py b/predict.py new file mode 100644 index 0000000..40beb4e --- /dev/null +++ b/predict.py @@ -0,0 +1,61 @@ + +import pandas as pd +import torch +import torch.nn as nn +import itertools + +from sklearn.preprocessing import MinMaxScaler +from sklearn.metrics import f1_score as f1 +from sklearn.metrics import confusion_matrix +from sklearn.metrics import * + +from torch.utils.data import TensorDataset +from torch.utils.data import DataLoader + +test_file = pd.read_csv('test.data').drop('Unnamed: 0', axis=1) +df_test = test_file.dropna() + +X_test = df_test.drop('class', axis=1) +Y_test = df_test['class'] + +scaler = MinMaxScaler() +X_test = scaler.fit_transform(X_test) + +x_test_tensor = torch.tensor(X_test).float() +y_test_tensor = torch.tensor(Y_test.values).float() + +test_ds = TensorDataset(x_test_tensor, y_test_tensor.unsqueeze(1)) +test_dl = DataLoader(test_ds, batch_size=64) + +model = torch.jit.load('model_scripted.pt') +model.eval() + +y_pred_list = [] +model.eval() + +with torch.no_grad(): + for xb_test,yb_test in test_dl: + y_test_pred = model(xb_test) + y_pred_tag = torch.round(y_test_pred) + y_pred_list.append(y_pred_tag.detach().numpy()) + +y_pred_list = [a.squeeze().tolist() for a in y_pred_list] + +ytest_pred = list(itertools.chain.from_iterable(y_pred_list)) + +y_true_test = Y_test.values.ravel() +conf_matrix = confusion_matrix(y_true_test ,ytest_pred) +print("Confusion Matrix of the Test Set") +print("-----------") +print(conf_matrix) +print("Precision of the MLP :\t"+str(precision_score(y_true_test,ytest_pred))) +print("Accuracy of the MLP :\t"+str(accuracy_score(y_true_test,ytest_pred))) +print("Recall of the MLP :\t"+str(recall_score(y_true_test,ytest_pred))) +print("F1 Score of the Model :\t"+str(f1_score(y_true_test,ytest_pred))) + + +print(*map(int, ytest_pred)) +print(*y_true_test) + +with open('predicted_values.txt', 'w') as f: + f.write(" ".join(map(str, map(int, ytest_pred)))) \ No newline at end of file diff --git a/test.data b/test.data new file mode 100644 index 0000000..af5ebbe --- /dev/null +++ b/test.data @@ -0,0 +1,116 @@ 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DataLoader + +from sacred import Experiment +from sacred.observers import FileStorageObserver, MongoObserver + +exint = Experiment("ium_z487186", interactive=True) +exint.observers.append(FileStorageObserver('my_runs')) +exint.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', + db_name='sacred')) + +@exint.config +def my_config(): + batch_size = 64 + learning_rate = 0.001 + epochs = 100 + + +@exint.capture +def prepare_message(msg): + return msg + + +@exint.main +def my_main(batch_size, learning_rate, epochs): + train_file = pd.read_csv('train.data').drop('Unnamed: 0', axis=1) + df_pandas = train_file.dropna() + + X_train = df_pandas.drop('class', axis=1) + Y_train = df_pandas['class'] + + scaler = MinMaxScaler() + X_train = scaler.fit_transform(X_train) + + x_tensor = torch.tensor(X_train).float() + y_tensor = torch.tensor(Y_train.values).float() + + train_ds = TensorDataset(x_tensor, y_tensor.unsqueeze(1)) + train_dl = DataLoader(train_ds, batch_size=batch_size) + + class ClassificationModel(nn.Module): + def __init__(self, n_input_dim): + super(ClassificationModel, self).__init__() + self.layer_1 = nn.Linear(n_input_dim, 256) + self.layer_2 = nn.Linear(256, 128) + self.layer_out = nn.Linear(128, 1) + + self.relu = nn.ReLU() + self.sigmoid = nn.Sigmoid() + self.dropout = nn.Dropout(p=0.1) + self.batchnorm1 = nn.BatchNorm1d(256) + self.batchnorm2 = nn.BatchNorm1d(128) + + def forward(self, inputs): + x = self.relu(self.layer_1(inputs)) + x = self.batchnorm1(x) + x = self.relu(self.layer_2(x)) + x = self.batchnorm2(x) + x = self.dropout(x) + x = self.sigmoid(self.layer_out(x)) + + return x + + + model = ClassificationModel(X_train.shape[1]) + print(model) + + + loss_func = nn.BCEWithLogitsLoss() + optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) + + model.train() + train_loss = [] + + for epoch in range(epochs): + for xb, yb in train_dl: + y_pred = model(xb) # Forward Propagation + loss = loss_func(y_pred, yb) # Loss Computation + optimizer.zero_grad() # Clearing all previous gradients, setting to zero + loss.backward() # Back Propagation + optimizer.step() # Updating the parameters + + if epoch % 10 == 0: + print(f"Loss in {epoch}. iteration: {loss.item()}") + train_loss.append(loss.item()) + print('Last iteration loss value: '+str(loss.item())) + + + model_scripted = torch.jit.script(model) # Export to TorchScript + model_scripted.save('model_scripted.pt') # Save + + +exint.run() +