Merge pull request #30 from machine-data-hub/final

Final
machine-data-hub · May 27, 2021 · f0f84fb · f0f84fb
2 parents 4ad4753 + bd51992
commit f0f84fb
Show file tree

Hide file tree

Showing 4 changed files with 25 additions and 21 deletions.
diff --git a/README.md b/README.md
@@ -1,4 +1,6 @@
 [![Documentation Status](https://readthedocs.org/projects/machine-data-hub/badge/?version=latest)](https://machine-data-hub.readthedocs.io/en/latest/?badge=latest)
 
 # Machine Data Hub
-Python Client to use the [Machine Data Hub](https://machinedatahub.ai).
+Python Client to use the [Machine Data Hub](https://machinedatahub.ai), an open source project
+to make state of the art machine learning and artificial intelligence techniques more accessible to engineering domains 
+by providing suitable benchmark datasets for advancing prognostics
diff --git a/pyproject.toml b/pyproject.toml
@@ -2,6 +2,10 @@
 name = "machine-data-hub"
 version = "0.1.0"
 description = "Python-based Command Line Tool to use the Machine Data Hub"
+homepage = "https://machinedatahub.ai/"
+repository = "https://github.com/machine-data-hub"
+documentation = "https://machine-data-hub.readthedocs.io/en/latest/?badge=latest"
+readme = "README.md"
 authors = ["ceciliabarnes <cbarnes7@uw.edu>"]
 license = "MIT License"
 

diff --git a/src/machine_data_hub/cli.py b/src/machine_data_hub/cli.py
@@ -32,7 +32,7 @@ def dataset_names(datasets):
         + " ("
         + str(len(row["Datasets"]))
         + " files, "
-        + row["File Size"]
+        + row["FileSize"]
         + ")"
         for row in datasets
     ]
@@ -176,14 +176,9 @@ def metadata(id: int):
                             ),
                         ]
                         table.append(info)
-                    elif key == "Summary":
-                        sep = "\n"
-                        row[key] = sep.join(textwrap.wrap(row["Summary"], width=90))
-                        info = [key, row[key]]
-                        table.append(info)
                     elif (
                         key == "img_link"
-                        or key == "One Line"
+                        or key == "Summary"
                         or key == "URL"
                         or key == "Rank"
                     ):

diff --git a/tests/test_cli.py b/tests/test_cli.py
@@ -10,32 +10,35 @@
 
 MOCK_DATASETS = [
     {
-        "id": 1,
+        "id": "1",
         "Rank": 1,
-        "Name": "Combined Cycle Power Plant Data Set",
-        "Owner": "UC Irvine",
-        "URL": "https://archive.ics.uci.edu/ml/machine-learning-databases/00294/CCPP.zip",
-        "Short Summary": "The dataset contains 9568 data points collected from a Combined Cycle Power Plant over 6 years (2006-2011), when the power plant was set to work with full load. Features consist of hourly average ambient variables Temperature (T), Ambient Pressure (AP), Relative Humidity (RH) and Exhaust Vacuum (V) to predict the net hourly electrical energy output (EP) of the plant. A combined cycle power plant (CCPP) is composed of gas turbines (GT), steam turbines (ST) and heat recovery steam generators. In a CCPP, the electricity is generated by gas and steam turbines, which are combined in one cycle, and is transferred from one turbine to another. While the Vacuum is collected from and has effect on the Steam Turbine, he other three of the ambient variables effect the GT performance. For comparability with our baseline studies, and to allow 5x2 fold statistical tests be carried out, we provide the data shuffled five times. For each shuffling 2-fold CV is carried out and the resulting 10 measurements are used for statistical testing.",
+        "Name": "Combined Cycle Power Plant",
+        "Owner": "University of California Irvine",
+        "DateDonated": "January 20, 2021",
+        "URL": "https://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant",
+        "ShortSummary": "The dataset contains 9568 data points collected from a Combined Cycle Power Plant over 6 years (2006-2011), when the power plant was set to work with full load. Features consist of hourly average ambient variables Temperature (T), Ambient Pressure (AP), Relative Humidity (RH) and Exhaust Vacuum (V) to predict the net hourly electrical energy output (EP) of the plant. A combined cycle power plant (CCPP) is composed of gas turbines (GT), steam turbines (ST) and heat recovery steam generators. In a CCPP, the electricity is generated by gas and steam turbines, which are combined in one cycle, and is transferred from one turbine to another. While the Vacuum is collected from and has effect on the Steam Turbine, he other three of the ambient variables effect the GT performance. For comparability with our baseline studies, and to allow 5x2 fold statistical tests be carried out, we provide the data shuffled five times. For each shuffling 2-fold CV is carried out and the resulting 10 measurements are used for statistical testing.",
+        "OneLine": "Data collected from a Combined Power Plant working full load over 6 years.",
+        "FileType": "xlsx",
         "Sector": "Power",
-        "ML Type": "Regression",
+        "MLType": ["Regression"],
         "Labeled": "Yes",
-        "Time Series": "No",
-        "Simulation (Yes/No)": "N/A",
+        "TimeSeries": "No",
+        "Simulation": "",
         "Attributes": 4,
         "Instances": 9568,
         "Downloads": 191037,
         "Likes": 0,
-        "File Size": "3.7 MB",
-        "img_link": "https://www.miga.org/sites/default/files/2018-06/power-plant-bright-blue-sky.jpg",
+        "FileSize": "3.7 MB",
+        "ImgLink": "/images/power-plant-bright-blue-sky.jpg",
         "Datasets": [
             {
-                "Name": "Dataset 1",
+                "Name": "File 1",
                 "URL": "https://archive.ics.uci.edu/ml/machine-learning-databases/00294/CCPP.zip",
                 "Likes": 0,
                 "Downloads": 191037,
-                "File Size": "3.7 MB",
+                "FileSize": "3.7 MB"
             }
-        ],
+        ]
     }
 ]