Make tutorial file paths local (#42)

datasciencecampus · Apr 3, 2024 · e6f3dfe · e6f3dfe
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Showing 2 changed files with 7 additions and 14 deletions.
diff --git a/docs/tutorials/example-febrl.qmd b/docs/tutorials/example-febrl.qmd
@@ -20,8 +20,6 @@ from recordlinkage.datasets import load_febrl4
 
 from pprl import EmbeddedDataFrame, Embedder, config
 from pprl.embedder import features as feat
-
-datadir = config.DIR_DATA_INTERIM
 ```
 
 ## Load the data
@@ -45,9 +43,6 @@ feb4b["true_id"] = (
     .iloc[:, 0].astype("int")
     .to_list()
 )
-
-feb4a.to_csv(os.path.join(datadir, "febrl_data_1.csv"))
-feb4b.to_csv(os.path.join(datadir, "febrl_data_2.csv"))
 ```
 
 ## Create a feature factory
@@ -128,9 +123,9 @@ edf2 = embedder.embed(feb4b, colspec=colspec)
 Store the embedded datasets and their embedder to file.
 
 ```{python}
-edf1.to_json(os.path.join(datadir, "party1_data.json"))
-edf2.to_json(os.path.join(datadir, "party2_data.json"))
-embedder.to_pickle(os.path.join(datadir, "embedder.pkl"))
+edf1.to_json("party1_data.json")
+edf2.to_json("party2_data.json")
+embedder.to_pickle("embedder.pkl")
 ```
 
 ## Calculate similarity

diff --git a/docs/tutorials/run-through.qmd b/docs/tutorials/run-through.qmd
@@ -21,8 +21,6 @@ import pandas as pd
 
 from pprl import EmbeddedDataFrame, Embedder, config
 from pprl.embedder import features as feat
-
-filestem = config.DIR_DATA_INTERIM
 ```
 
 ## Data set-up
@@ -160,9 +158,9 @@ matching server. For this purpose, it's possible to pickle the entire
 `Embedder` object.
 
 ```{python}
-embedder.to_pickle(os.path.join(filestem, "embedder.pkl"))
+embedder.to_pickle("embedder.pkl")
 
-embedder_copy = Embedder.from_pickle(os.path.join(filestem, "embedder.pkl"))
+embedder_copy = Embedder.from_pickle("embedder.pkl")
 ```
 
 The copy has the same functionality as the original:
@@ -194,9 +192,9 @@ The EDF objects are just a thin wrapper around `pandas.DataFrame` instances, so
 you can serialise to JSON using the normal methods.
 
 ```{python}
-edf1.to_json(os.path.join(filestem, "edf1.json"))
+edf1.to_json("edf1.json")
 
-edf1_copy = pd.read_json(os.path.join(filestem, "edf1.json"))
+edf1_copy = pd.read_json("edf1.json")
 
 print(isinstance(edf1_copy,EmbeddedDataFrame))
 print(isinstance(edf1_copy,pd.DataFrame))