add jnks,etc

2023-06-28 22:39:02 +02:00 · 2023-06-28 22:39:02 +02:00 · 8c2f6e4e0f
parent 493b2e6e37
commit 8c2f6e4e0f
6 changed files with 220 additions and 39 deletions
--- a/.DS_Store
+++ b/.DS_Store
--- a/18
+++ b/18
@ -1,14 +1,6 @@
 FROM ubuntu:latest
-RUN apt-get update \
-    && apt-get install -y git python3 python3-pip curl \
-    && curl -O https://bootstrap.pypa.io/get-pip.py \
-    && python3 get-pip.py --user \
-    && rm get-pip.py \
-    && pip3 install --user kaggle \
-    && pip3 install --user pandas \
-    && pip3 install --user seaborn \
-    && pip3 install --user scikit-learn
-ENV PATH="/root/.local/bin:$PATH"
-WORKDIR /app
-COPY . /app
-CMD ["python", "create_dataset.py"]
+
+RUN apt-get update && \
+    apt-get install -y python3-pip python3-dev && \
+    apt-get install -y build-essential && \
+    pip3 install pandas kaggle seaborn scikit-learn torch matplotlib \
--- a/79
+++ b/79
@ -1,29 +1,56 @@
-node {
-    stage('Preparation') { 
-        properties([
-            parameters([
-                string(
-                    defaultValue: 'bartekmalanka',
-                    description: 'Kaggle username',
-                    name: 'KAGGLE_USERNAME',
-                    trim: false
-                ),
-                password(
-                    defaultValue: '',
-                    description: 'Kaggle token taken from kaggle.json file, as described in https://github.com/Kaggle/kaggle-api#api-credentials',
-                    name: 'KAGGLE_KEY'
-                )
-            ])
-        ])
-    }
-    stage('Build') {
-        // Run the maven build
-        withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
-                 "KAGGLE_KEY=${params.KAGGLE_KEY}" ]) {
-             sh 'kaggle datasets download -d elakiricoder/gender-classification-dataset > output.txt'
+pipeline {
+   agent any
+   parameters {
+     string(
+            defaultValue: 'wojciechbatruszewicz',
+            description: 'Kaggle username',
+            name: 'KAGGLE_USERNAME',
+            trim: false
+         )
+      password(
+         defaultValue: '',
+         description: 'Kaggle token taken from kaggle.json file, as described in https://github.com/Kaggle/kaggle-api#api-credentials',
+         name: 'KAGGLE_KEY'
+      )
+      string(
+            defaultValue: '30',
+            description: 'dataset cutoff',
+            name: 'CUTOFF',
+            trim: false
+         )
+   }
+   stages {
+      stage('Download dataset') {
+         steps {
+            checkout scm
            sh 'ls -l'
-             archiveArtifacts artifacts: 'gender_classification_v7.csv, output.txt'
-
+            withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
+                "KAGGLE_KEY=${params.KAGGLE_KEY}" ]) {
+                sh 'kaggle datasets download -d elakiricoder/gender-classification-dataset'
+                sh 'unzip -o gender-classification-dataset.zip'
+            }
+         }
+      }
+      stage('Docker') {
+        steps {
+               script {
+                  def dockerImage = docker.build("docker-image", "./")
+                  dockerImage.inside {
+                        sh 'ls -l'
+                        sh 'ls -l'
+                        sh 'python3 createDataset.py'
+                        archiveArtifacts 'gender_classification_train.csv'
+                        archiveArtifacts 'gender_classification_test.csv'
+                        archiveArtifacts 'gender_classification_val.csv'
+                     sh 'ls -l'
+                  }  
+            }
+        }
+      }           
+   }
+   post {
+        success {
+            build job: 'x1-training/main', wait: false
        }
    }
-}
+}
--- a/evaluate.py
+++ b/evaluate.py
@ -0,0 +1,80 @@
+import torch
+from train import MyNeuralNetwork, load_data
+from torch.utils.data import DataLoader
+import csv
+import os
+import matplotlib.pyplot as plt
+from typing import Tuple, List
+
+def evaluate_model() -> Tuple[List[float], float]:
+    model = MyNeuralNetwork()
+    model.load_state_dict(torch.load('model.pt'))
+    model.eval()
+    test_dataset = load_data("gender_classification_test.csv")
+    batch_size: int = 32
+    test_dataloader: DataLoader = DataLoader(test_dataset, batch_size=batch_size)
+    predictions = []
+    labels = []
+    get_label = lambda pred: 1 if pred >= 0.5 else 0
+    total = 0
+    correct = 0
+    with torch.no_grad():
+        for batch_data, batch_labels in test_dataloader:
+            batch_predictions = model(batch_data)
+            predicted_batch_labels = [get_label(prediction) for prediction in batch_predictions]
+            total += len(predicted_batch_labels)
+            batch_labels_list = list(map(int,batch_labels.tolist()))
+            correct += sum(x == y for x, y in zip(predicted_batch_labels, batch_labels_list))
+            predictions.extend(batch_predictions)
+            labels.extend(batch_labels)
+    accuracy = correct/total
+    return predictions, accuracy
+
+def save_predictions(predictions: list[float]) -> None:
+    filename = "results.csv"
+    column_name = "predict"
+    with open(filename, 'w', newline='') as file:
+        writer = csv.writer(file)
+        writer.writerow([column_name])
+        for result in predictions:
+            loan_decision = 1 if result.item() > 0.5 else 0
+            writer.writerow([loan_decision])
+
+def save_accuracy(accuracy):
+    filename = 'results.csv'
+    if os.path.exists(filename):
+        with open(filename, 'a') as file:
+            writer = csv.writer(file)
+            writer.writerow([accuracy])
+    else:
+        with open(filename, 'w') as file:
+            writer = csv.writer(file)
+            writer.writerow(['accuracy'])
+            writer.writerow([accuracy])
+        
+def plot_accuracy():
+    filename = 'results.csv'
+    accuracy_results = []
+    if os.path.exists(filename):
+        with open(filename, 'r') as file:
+            reader =  csv.reader(file)
+            for idx, row in enumerate(reader):
+                if idx == 0:
+                    continue
+                accuracy_results.append(float(row[0]))
+    iterations = list(map(str,range(1, len(accuracy_results)+1)))
+    plt.plot(iterations, accuracy_results)
+    plt.xlabel('build')
+    plt.ylabel('accuracy')
+    plt.title("Accuracies over builds.")
+    plt.savefig("plot.png")
+                
+def main():
+    predictions, accuracy = evaluate_model()
+    save_predictions(predictions)
+    save_accuracy(accuracy)
+    plot_accuracy()
+    
+    
+if __name__ == "__main__":
+    main()
--- a/model.pt
+++ b/model.pt
--- a/train.py
+++ b/train.py
@ -0,0 +1,82 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader, TensorDataset
+import pandas as pd
+from sklearn.preprocessing import LabelBinarizer
+import numpy as np
+import argparse
+
+
+class MyNeuralNetwork(nn.Module):
+    def __init__(self, *args, **kwargs) -> None:
+        super(MyNeuralNetwork, self).__init__(*args, **kwargs)
+        self.fc1 = nn.Linear(7, 12)
+        self.relu = nn.ReLU()
+        self.fc1 = nn.Linear(7, 12)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(12, 1)
+        self.sigmoid = nn.Sigmoid()
+    
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.sigmoid(x)
+        return x
+
+def prepare_df_for_nn(df):
+    
+    id_column_name_list = [column for column in df.columns.to_list() if 'id' in  column.lower()]
+    if len(id_column_name_list) == 0:
+        pass
+    else:
+        df.drop(id_column_name_list[0], inplace=True, axis=1)
+    encoder =  LabelBinarizer()
+    df.reset_index(inplace=True)
+    for column in df.columns:
+        if str(df[column].dtype).lower() == 'object':
+            encoded_column = encoder.fit_transform(df[column])
+            df[column] = pd.Series(encoded_column.flatten(), dtype=pd.Int16Dtype)
+    return df
+
+def load_data(path):
+    df = pd.read_csv(path) 
+    train_dataset = prepare_df_for_nn(df)
+    x = train_dataset.iloc[:, :-1].values.astype(float)
+    y = train_dataset.iloc[:, -1].values.astype(float)
+    x_tensor = torch.tensor(x, dtype=torch.float32)
+    y_tensor = torch.tensor(y, dtype=torch.float32)
+    dataset = TensorDataset(x_tensor, y_tensor)
+    return dataset
+
+def train(epochs, dataloader_train):
+    model: MyNeuralNetwork = MyNeuralNetwork()
+    criterion: nn.BCELoss = nn.BCELoss()
+    optimizer = optim.Adam(model.parameters(), lr=0.001)
+    for epoch in range(epochs):
+        for inputs, labels in dataloader_train:
+            outputs = model(inputs)
+            labels = labels.reshape((labels.shape[0], 1))
+            loss = criterion(outputs, labels)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+        print(f"Epoch {epoch+1}/{epochs}, Loss: {loss.item():.4f}")
+        
+    return model
+
+def main():
+    parser = argparse.ArgumentParser(description='A test program.')
+    parser.add_argument("--epochs", help="Prints the supplied argument.", default='10')
+    args = parser.parse_args()
+    config = vars(args)
+    epochs =  int(config["epochs"])  
+    train_dataset = load_data("gender_classification_train.csv")
+    batch_size = 32
+    dataloader_train = DataLoader(train_dataset, batch_size = batch_size, shuffle=True)
+    model = train(epochs, dataloader_train)
+    torch.save(model.state_dict(), 'model.pt')
+    
+if __name__ == "__main__":
+    main()