break down long commands
Split into multiple lines to make it more readable on github.com
@@ -51,15 +51,33 @@ the train and predict tasks but also a lot of data preprocessing.
#### Training task
```sh
-python assets/algo_random_forest/algo.py train --debug --opener-path assets/dataset/opener.py --data-samples-path assets/train_data_samples --output-predictions-path assets/pred-train.csv
-python assets/objective/metrics.py --debug --opener-path assets/dataset/opener.py --data-samples-path assets/train_data_samples --input-predictions-path assets/pred-train.csv
+python assets/algo_random_forest/algo.py train \
+ --debug \
+ --opener-path assets/dataset/opener.py \
+ --data-samples-path assets/train_data_samples \
+ --output-predictions-path assets/pred-train.csv
+
+python assets/objective/metrics.py \
+ --debug \
+ --opener-path assets/dataset/opener.py \
+ --data-samples-path assets/train_data_samples \
+ --input-predictions-path assets/pred-train.csv
```
#### Testing task
```sh
-python assets/algo_random_forest/algo.py predict --debug --opener-path assets/dataset/opener.py --data-samples-path assets/test_data_samples --output-predictions-path assets/pred-test.csv model
-python assets/objective/metrics.py --debug --opener-path assets/dataset/opener.py --data-samples-path assets/test_data_samples --input-predictions-path assets/pred-test.csv
+python assets/algo_random_forest/algo.py predict \
+ --debug \
+ --opener-path assets/dataset/opener.py \
+ --data-samples-path assets/test_data_samples \
+ --output-predictions-path assets/pred-test.csv model
+
+python assets/objective/metrics.py \
+ --debug \
+ --opener-path assets/dataset/opener.py \
+ --data-samples-path assets/test_data_samples \
+ --input-predictions-path assets/pred-test.csv
```
### Using substra cli
@@ -71,7 +89,12 @@ To test the assets, we'll use `substra run-local`, passing it paths to our algor
the metrics and to the data samples we want to use.
```sh
-substra run-local assets/algo_random_forest --train-opener=assets/dataset/opener.py --test-opener=assets/dataset/opener.py --metrics=assets/objective/ --train-data-samples=assets/train_data_samples --test-data-samples=assets/test_data_samples
+substra run-local assets/algo_random_forest \
+ --train-opener=assets/dataset/opener.py \
+ --test-opener=assets/dataset/opener.py \
+ --metrics=assets/objective/ \
+ --train-data-samples=assets/train_data_samples \
+ --test-data-samples=assets/test_data_samples
```
At the end of this step, you'll find in the newly created `sandbox/model` folder a `model` file that contains your
@@ -136,7 +159,11 @@ but they help identify the related assets).
Now we can launch the `run-local` using fake data:
```sh
-substra run-local assets/algo_constant --train-opener=assets/dataset/opener.py --test-opener=assets/dataset/opener.py --metrics=assets/objective/ --fake-data-samples
+substra run-local assets/algo_constant \
+ --train-opener=assets/dataset/opener.py \
+ --test-opener=assets/dataset/opener.py \
+ --metrics=assets/objective/ \
+ --fake-data-samples
```
Once again, we can use the `logging` module to debug.
