add evaluation

Jenkinsfile_eval

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+pipeline {
+  	agent {
+		docker {
+			image 'docker_image'
+		}
+	}
+	parameters {
+	    gitParameter branchFilter: 'origin/(.*)', defaultValue: 'master', name: 'BRANCH', type: 'PT_BRANCH'
+		buildSelector(
+			defaultSelector: lastSuccessful(),
+			description: 'Which build to use for copying artifacts',
+			name: 'BUILD_SELECTOR'
+		)
+	}
+   stages {
+	stage('Script'){
+		steps {
+                copyArtifacts filter: '*', projectName: 's444018-create-dataset', selector: buildParameter('BUILD_SELECTOR')
+                copyArtifacts filter: '*', projectName: 's444018-training/${BRANCH}', selector: buildParameter('BUILD_SELECTOR')
+                copyArtifacts filter: '*', projectName: 's444018-evaluation/master', selector: buildParameter('BUILD_SELECTOR'), optional: true
+			          sh 'python3 ./biblioteka_DL/evaluate.py'
+			          archiveArtifacts artifacts: 'mae.txt, rmse.txt, mse.txt, evr.txt, metrics.png', followSymlinks: false
+			script {
+                       	    	ACC = sh (
+                            	script: 'tail -1 metrics.txt',
+                        	returnStdout: true
+                            	).trim()
+                    	}
+		}
+	}
+    }
+    post {
+        success {
+            emailext body: "SUCCESS, ACC = ${ACC}", subject: 's444018-evaluation', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        failure {
+            emailext body: "FAILURE, ACC = ${ACC}", subject: 's444018-evaluation', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        unstable {
+            emailext body: "UNSTABLE, ACC = ${ACC}", subject: 's444018-evaluation', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        changed {
+            emailext body: "CHANGED, ACC = ${ACC}", subject: 's444018-evaluation', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+    }
+}

biblioteka_DL/evaluate.py

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+import sys
+import torch
+import torch.nn as nn
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support, explained_variance_score, \
+    mean_squared_error, mean_absolute_error
+
+
+def drop_relevant_columns(imbd_data):
+    imbd_data.drop(columns=["Poster_Link"], inplace=True)
+    imbd_data.drop(columns=["Overview"], inplace=True)
+    imbd_data.drop(columns=["Certificate"], inplace=True)
+    return imbd_data
+
+
+def lowercase_columns_names(imbd_data):
+    imbd_data["Series_Title"] = imbd_data["Series_Title"].str.lower()
+    imbd_data["Genre"] = imbd_data["Genre"].str.lower()
+    imbd_data["Director"] = imbd_data["Director"].str.lower()
+    imbd_data["Star1"] = imbd_data["Star1"].str.lower()
+    imbd_data["Star2"] = imbd_data["Star2"].str.lower()
+    imbd_data["Star3"] = imbd_data["Star3"].str.lower()
+    imbd_data["Star4"] = imbd_data["Star4"].str.lower()
+    return imbd_data
+
+
+def data_to_numeric(imbd_data):
+    imbd_data = imbd_data.replace(np.nan, '', regex=True)
+    imbd_data["Gross"] = imbd_data["Gross"].str.replace(',', '')
+    imbd_data["Gross"] = pd.to_numeric(imbd_data["Gross"], errors='coerce')
+    imbd_data["Runtime"] = imbd_data["Runtime"].str.replace(' min', '')
+    imbd_data["Runtime"] = pd.to_numeric(imbd_data["Runtime"], errors='coerce')
+    imbd_data["IMDB_Rating"] = pd.to_numeric(imbd_data["IMDB_Rating"], errors='coerce')
+    imbd_data["Meta_score"] = pd.to_numeric(imbd_data["Meta_score"], errors='coerce')
+    imbd_data["Released_Year"] = pd.to_numeric(imbd_data["Released_Year"], errors='coerce')
+    imbd_data = imbd_data.dropna()
+    imbd_data = imbd_data.reset_index()
+    imbd_data.drop(columns=["index"], inplace=True)
+    return imbd_data
+
+
+def create_train_dev_test(imbd_data):
+    data_train, data_test = train_test_split(imbd_data, test_size=230, random_state=1, shuffle=True)
+    data_test, data_dev = train_test_split(data_test, test_size=115, random_state=1, shuffle=True)
+    data_test.to_csv("data_test.csv", encoding="utf-8", index=False)
+    data_dev.to_csv("data_dev.csv", encoding="utf-8", index=False)
+    data_train.to_csv("data_train.csv", encoding="utf-8", index=False)
+
+
+def normalize_gross(imbd_data):
+    imbd_data[["Gross"]] = imbd_data[["Gross"]] / 10000000
+    return imbd_data
+
+
+def prepare_dataset():
+    df = pd.read_csv('biblioteka_DL/imdb_top_1000.csv')
+    df = drop_relevant_columns(df)
+    df_lowercase = lowercase_columns_names(df)
+    df = data_to_numeric(df_lowercase)
+    df = normalize_gross(df)
+    return df
+
+
+class LinearRegressionModel(torch.nn.Module):
+
+    def __init__(self):
+        super(LinearRegressionModel, self).__init__()
+        self.linear = torch.nn.Linear(1, 1)  # One in and one out
+
+    def forward(self, x):
+        y_pred = self.linear(x)
+        return y_pred
+
+
+df = prepare_dataset()
+data_train, data_test = train_test_split(df, random_state=1, shuffle=True)
+
+X_train = pd.DataFrame(data_train["Meta_score"], dtype=np.float64)
+X_train = X_train.to_numpy()
+
+y_train = pd.DataFrame(data_train["Gross"], dtype=np.float64)
+y_train = y_train.to_numpy()
+
+X_train = X_train.reshape(-1, 1)
+y_train = y_train.reshape(-1, 1)
+
+X_train = torch.from_numpy(X_train.astype(np.float32)).view(-1, 1)
+y_train = torch.from_numpy(y_train.astype(np.float32)).view(-1, 1)
+
+input_size = 1
+output_size = 1
+
+model = torch.load("model.pkl")
+
+X_test = pd.DataFrame(data_test["Meta_score"], dtype=np.float64)
+X_test = X_test.to_numpy()
+X_test = X_test.reshape(-1, 1)
+X_test = torch.from_numpy(X_test.astype(np.float32)).view(-1, 1)
+
+predicted = model(X_test).detach().numpy()
+
+gross_test_g = pd.DataFrame(data_test["Gross"], dtype=np.float64)
+gross_test_g = gross_test_g.to_numpy()
+gross_test_g = gross_test_g.reshape(-1, 1)
+
+pred = pd.DataFrame(predicted)
+
+predicted = []
+expected = []
+
+for i in range(0, len(X_test)):
+    predicted.append(np.argmax(model(X_test[i]).detach().numpy(), axis=0))
+    expected.append(gross_test_g[i])
+
+for i in range(0, len(expected)):
+    expected[i] = expected[i][0]
+
+rmse = mean_squared_error(gross_test_g, pred, squared=False)
+mse = mean_squared_error(gross_test_g, pred)
+evr = explained_variance_score(gross_test_g, pred)
+mae = mean_absolute_error(gross_test_g, pred)
+
+res = f"Explained variance regression score: {evr}, RMSE: {rmse}, MSE: {mse}, MAE: {mae}"
+
+with open('mae.txt', 'a+') as f:
+    f.write(str(mae) + '\n')
+
+with open('rmse.txt', 'a+') as f:
+    f.write(str(rmse) + '\n')
+
+with open('mse.txt', 'a+') as f:
+    f.write(str(mse) + '\n')
+
+with open('evr.txt', 'a+') as f:
+    f.write(str(evr) + '\n')
+
+with open('mae.txt') as f:
+    mae_val = [float(line) for line in f if line]
+    builds = list(range(1, len(mae_val) + 1))
+
+with open('rmse.txt') as f:
+    rmse_val = [float(line) for line in f if line]
+
+with open('mse.txt') as f:
+    mse_val = [float(line) for line in f if line]
+
+with open('evr.txt') as f:
+    evr_val = [float(line) for line in f if line]
+
+
+ax = plt.gca()
+ax.set_title('Build')
+
+mae_line = ax.plot(mae_val, color='blue', label="MAE")
+rmse_line = ax.plot(rmse_val, color='green', label="RMSE")
+mse_line = ax.plot(mse_val, color='red', label="MSE")
+evr_line = ax.plot(evr_val, color='orange', label="EVR")
+ax.legend(bbox_to_anchor=(0., 1.01, 1.0, .1), loc=3,
+           ncol=2, mode="expand", borderaxespad=0.)
+plt.show()
+plt.savefig('metrics.png')