commit c97a233bd3529c9bc212e3ce83c152548472807d
Author: wojciechbatruszewicz <wojtek.baltruszewicz@gmail.com>
Date:   Mon Jun 26 18:49:28 2023 +0200

    Create dataset

diff --git a/Dockerfile b/Dockerfile
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+++ b/Dockerfile
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+FROM ubuntu:latest
+
+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 \
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diff --git a/Jenkinsfile b/Jenkinsfile
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+++ b/Jenkinsfile
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+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
+            dir ('./createDataset') {
+               sh 'ls -l'
+               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.zip'
+               }
+            }
+         }
+      }
+      stage('Docker') {
+        steps {
+               script {
+                  def dockerImage = docker.build("docker-image", "./docker")
+                  dockerImage.inside {
+                     sh 'ls -l'
+                     dir ('./createDataset') {
+                        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'
+                  }  
+            }
+        }
+      }
+
+      // stage('Archive file') {
+      //    steps {
+      //       dir ('./createDataset') {
+      //          archiveArtifacts artifacts: 'loan_sanction_shuffled.csv', fingerprint: true\
+      //       }
+      //    }
+      // }              
+   }
+//    post {
+//         success {
+//             build job: 'z-s487179-training/main', wait: false
+//         }
+//     }
+}
diff --git a/createDataset.py b/createDataset.py
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+++ b/createDataset.py
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+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import train_test_split
+gender_classification = pd.read_csv('gender_classification_v7.csv.csv')
+
+gender_classification_train_final, gender_classification_test = train_test_split(gender_classification, test_size=0.2, random_state=1)
+gender_classification_test_final, gender_classification_val_final = train_test_split(gender_classification_test, test_size=0.5, random_state=1)
+
+numeric_cols_train = gender_classification_train_final.select_dtypes(include='number').columns
+numeric_cols_test = gender_classification_test_final.select_dtypes(include='number').columns
+numeric_cols_val = gender_classification_val_final.select_dtypes(include='number').columns
+
+scaler = MinMaxScaler()
+
+gender_classification_train_final[numeric_cols_train] = scaler.fit_transform(gender_classification_train_final[numeric_cols_train])
+gender_classification_test_final[numeric_cols_test] = scaler.fit_transform(gender_classification_test_final[numeric_cols_test])
+gender_classification_val_final[numeric_cols_val] = scaler.fit_transform(gender_classification_val_final[numeric_cols_val])
+
+gender_classification_train_final = gender_classification_train_final.dropna()
+gender_classification_test_final = gender_classification_test_final.dropna()
+gender_classification_val_final = gender_classification_val_final.dropna()
+
+gender_classification_train_final.to_csv('gender_classification_train.csv', index=False)
+gender_classification_test_final.to_csv('gender_classification_test.csv', index=False)
+gender_classification_val_final.to_csv('gender_classification_val.csv', index=False)
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