From 0196c8c5a3cedd2c45619231809f3109522db53d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Szymon=20Parafin=CC=81ski?= <sparafinski@fandom.com>
Date: Sun, 5 Jun 2022 23:45:15 +0200
Subject: [PATCH] maybe lab6

---
 Dockerfile                     | 4 ++--
 dvc.yaml                       | 4 ++--
 lab6/biblioteka_DL/dllib.py    | 2 +-
 lab6/biblioteka_DL/evaluate.py | 2 +-
 4 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index 53ae0a6..892aacb 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -27,8 +27,8 @@ ENV KAGGLE_KEY=${KAGGLE_KEY}
 WORKDIR /app
 
 COPY lab2/download.sh .
-COPY lab7/biblioteka_DL/dllib.py .
-COPY lab7/biblioteka_DL/evaluate.py .
+COPY lab6/biblioteka_DL/dllib.py .
+COPY lab6/biblioteka_DL/evaluate.py .
 COPY biblioteka_DL/imdb_top_1000.csv .
 COPY predict.py .
 COPY registry.py .
diff --git a/dvc.yaml b/dvc.yaml
index f8bb33e..f7d4c53 100644
--- a/dvc.yaml
+++ b/dvc.yaml
@@ -1,9 +1,9 @@
 stages:
   train:
-    cmd: python3 ./lab7/biblioteka_DL/dllib.py 100
+    cmd: python3 ./lab6/biblioteka_DL/dllib.py 100
     deps:
     - data/imdb_top_1000.csv
   evaluate:
-    cmd: python3 ./lab7/biblioteka_DL/evaluate.py
+    cmd: python3 ./lab6/biblioteka_DL/evaluate.py
     deps:
     - data/imdb_top_1000.csv
diff --git a/lab6/biblioteka_DL/dllib.py b/lab6/biblioteka_DL/dllib.py
index 11f7499..1fc8a74 100644
--- a/lab6/biblioteka_DL/dllib.py
+++ b/lab6/biblioteka_DL/dllib.py
@@ -56,7 +56,7 @@ def normalize_gross(imbd_data):
 
 
 def prepare_dataset():
-    df = pd.read_csv('biblioteka_DL/imdb_top_1000.csv')
+    df = pd.read_csv('data/imdb_top_1000.csv')
     df = drop_relevant_columns(df)
     df_lowercase = lowercase_columns_names(df)
     df = data_to_numeric(df_lowercase)
diff --git a/lab6/biblioteka_DL/evaluate.py b/lab6/biblioteka_DL/evaluate.py
index 34b6acd..24ab926 100644
--- a/lab6/biblioteka_DL/evaluate.py
+++ b/lab6/biblioteka_DL/evaluate.py
@@ -56,7 +56,7 @@ def normalize_gross(imbd_data):
 
 
 def prepare_dataset():
-    df = pd.read_csv('biblioteka_DL/imdb_top_1000.csv')
+    df = pd.read_csv('data/imdb_top_1000.csv')
     df = drop_relevant_columns(df)
     df_lowercase = lowercase_columns_names(df)
     df = data_to_numeric(df_lowercase)