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lab05, lab07

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This commit is contained in:
Natalia Szymczyk 2023-06-30 17:22:42 +02:00
parent d9b911b767
commit 03a8b1321e
12 changed files with 945 additions and 2 deletions
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FROM ubuntu:latest 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" ENV HF_HOME="/tmp/hf"
COPY . .

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6
my_runs/1/config.json Normal file
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{
"batch_size": 64,
"epochs": 100,
"learning_rate": 0.001,
"seed": 758847465
}

24
my_runs/1/cout.txt Normal file
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INFO - sacred_scopes - Running command 'my_main'
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

1
my_runs/1/metrics.json Normal file
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{}

71
my_runs/1/run.json Normal file
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{
"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"
}

97
my_runs/_sources/train_f31ea0fb211226b21997ea6ed8f8699c.py Normal file
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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()

61
predict.py Normal file
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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))))

116
test.data Normal file
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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, 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()