reorganisation of file structure, fix eval and script.sh

Jenkinsfile

@@ -1,6 +1,6 @@
 node {
     checkout scm
-    def dockerimage = docker.build("titanic-image")
+    def dockerimage = docker.build("titanic-image", "dockerfile_titanic")
     dockerimage.inside {
  stage('Preparation') {
      properties([

JenkinsfileTrain

@@ -1,6 +1,7 @@
 node {
     checkout scm
-    def dockerimage = docker.build("train-image", "./train")
+    def dockerimage = docker.build("train-image", "dockerfile_train
+    ")
     dockerimage.inside {
  stage('Preparation') {
      properties([

dockerfile_titanic

@@ -0,0 +1,23 @@
+FROM ubuntu:latest
+
+RUN apt-get update --fix-missing
+RUN apt install python3-pip -y 
+RUN apt install unzip -y
+RUN apt install git -y
+
+RUN pip install --user kaggle
+RUN pip install --user pandas
+
+
+RUN ln -s ~/.local/bin/kaggle /usr/bin/kaggle
+ENV PATH="$PATH:~/.local/bin/kaggle"
+ENV KAGGLE_USERNAME="wiktorbombola"
+ENV KAGGLE_KEY=""
+
+# RUN echo "alias kaggle='~/.local/bin/kaggle'" >> ~/.bashrc
+
+COPY ./script.sh ./
+COPY ./learning.py ./
+
+# CMD ./script.sh 300
+# CMD ./learning.py

dockerfile_train

@@ -0,0 +1,19 @@
+FROM ubuntu:latest
+
+RUN apt-get update --fix-missing
+RUN apt install python3-pip -y 
+RUN apt install unzip -y
+RUN apt install git -y
+
+RUN pip install --user pandas
+RUN pip install --user torch
+RUN pip install --user keras
+RUN pip install --user tensorflow
+RUN pip install --user scikit-learn
+
+# RUN echo "alias kaggle='~/.local/bin/kaggle'" >> ~/.bashrc
+
+COPY ./../learning.py ./
+
+# CMD ./script.sh 300
+# CMD ./learning.pyRUN pip install --user numpy

eval.py

@@ -4,7 +4,7 @@ import torch
 from torch import nn
 import pandas as pd
 
-from sklearn.metrics import accuracy_score
+from sklearn.metrics import accuracy_score, precision_score, recall_score
 from sklearn.preprocessing import LabelEncoder
 import torch.nn.functional as F
 
@@ -39,5 +39,9 @@ x_test = torch.tensor(X.values, dtype=torch.float32)
 pred = model(x_test)
 pred = pred.detach().numpy()
 print ("The accuracy is", accuracy_score(Y, np.argmax(pred, axis=1)))
+print ("The precission score is ", precision_score(Y, np.argmax(pred, axis=1)))
+print ("The recall score is ", recall_score(Y, np.argmax(pred, axis=1)))
+
+
 
 np.savetxt('prediction.tsv', pred, delimiter='\t')

script.sh

@@ -3,5 +3,5 @@ kaggle competitions download -c titanic
 echo 'kaggle download completed'
 unzip titanic.zip
 wc -l train.csv
-head -$1 train.csv | shuf > output.csv
+head -n $1 train.csv  > output.csv
 echo 'script done'