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Karolina Oparczyk 2021-05-20 19:50:46 +02:00
parent b9f143d46c
commit 8251db9abf
3 changed files with 16 additions and 6 deletions
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1
Dockerfile
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@ -8,6 +8,7 @@ RUN pip3 install pandas
RUN pip3 install kaggle RUN pip3 install kaggle
RUN pip3 install tensorflow RUN pip3 install tensorflow
RUN pip3 install sklearn RUN pip3 install sklearn
RUN pip3 install matplotlib
COPY ./data_dev ./ COPY ./data_dev ./
COPY ./evaluate_network.py ./ COPY ./evaluate_network.py ./
RUN mkdir /.kaggle RUN mkdir /.kaggle

2
JenkinsfileEval
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@ -19,7 +19,7 @@ node {
} }
stage('Clone repo') { stage('Clone repo') {
docker.image("karopa/ium:16").inside { docker.image("karopa/ium:20").inside {
stage('Test') { stage('Test') {
checkout([$class: 'GitSCM', branches: [[name: '*/evaluation']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]]) checkout([$class: 'GitSCM', branches: [[name: '*/evaluation']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
copyArtifacts fingerprintArtifacts: true, projectName: 's434765-create-dataset', selector: buildParameter("BUILD_DATASET") copyArtifacts fingerprintArtifacts: true, projectName: 's434765-create-dataset', selector: buildParameter("BUILD_DATASET")

19
evaluate_network.py
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@ -2,15 +2,16 @@ import pandas as pd
import numpy as np import numpy as np
from sklearn.metrics import mean_squared_error from sklearn.metrics import mean_squared_error
from tensorflow import keras from tensorflow import keras
import matplotlib.pyplot as plt
model = keras.models.load_model('model') model = keras.models.load_model('model')
data = pd.read_csv("data_dev", sep=',', error_bad_lines=False, data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date", skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",
"publish_date", "publish_hour", "category_id", "publish_date", "publish_hour", "category_id",
"channel_title", "views", "likes", "dislikes", "channel_title", "views", "likes", "dislikes",
"comment_count"]).dropna() "comment_count"]).dropna()
X_test = data.loc[:,data.columns == "views"].astype(int) X_test = data.loc[:, data.columns == "views"].astype(int)
y_test = data.loc[:,data.columns == "likes"].astype(int) y_test = data.loc[:, data.columns == "likes"].astype(int)
min_val_sub = np.min(X_test) min_val_sub = np.min(X_test)
max_val_sub = np.max(X_test) max_val_sub = np.max(X_test)
@ -39,3 +40,11 @@ print(error)
with open("rmse.txt", "a") as file: with open("rmse.txt", "a") as file:
file.write(str(error) + "\n") file.write(str(error) + "\n")
with open("rmse.txt", "r") as file:
lines = file.readlines()
plt.plot(range(len(lines)), [line[:-2] for line in lines])
plt.tight_layout()
plt.ylabel('RMSE')
plt.xlabel('evaluation no')
plt.savefig('evaluation.png')