feat: adding examples for yesworkflow and noworkflow

benchmark/simple_ml_pipeline_yesworkflow/data-acquisition.py

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+#!/usr/bin/env python3
+
+# @BEGIN main
+# @PARAM data_url
+# @OUT raw_data @URI file:data/raw_data.csv
+# @OUT features @AS feature_names
+# @OUT target @AS target_variable
+
+import pandas as pd
+import numpy as np
+import os
+
+def acquire_data():
+    """
+    Acquires and processes the Boston Housing dataset.
+
+    @BEGIN fetch_raw_data
+    @IN data_url
+    @OUT raw_df @AS raw_dataset
+    @END fetch_raw_data
+
+    @BEGIN process_data
+    @IN raw_df @AS raw_dataset
+    @OUT data @AS feature_matrix
+    @OUT target @AS target_variable
+    @END process_data
+
+    @BEGIN create_features
+    @IN feature_names
+    @IN data @AS feature_matrix
+    @IN target @AS target_variable
+    @OUT X @AS feature_dataframe
+    @OUT y @AS target_series
+    @END create_features
+    """
+    print("Acquiring Boston Housing dataset...")
+
+    # @BEGIN fetch_raw_data
+    data_url = "http://lib.stat.cmu.edu/datasets/boston"
+    raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
+    # @END fetch_raw_data
+
+    # @BEGIN process_data
+    data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
+    target = raw_df.values[1::2, 2]
+    # @END process_data
+
+    # @BEGIN create_features
+    feature_names = [
+        'CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',
+        'TAX', 'PTRATIO', 'B', 'LSTAT'
+    ]
+    X = pd.DataFrame(data, columns=feature_names)
+    y = pd.Series(target, name='price')
+    # @END create_features
+
+    # @BEGIN save_data
+    # @IN X @AS feature_dataframe
+    # @IN y @AS target_series
+    # @OUT raw_data @URI file:data/raw_data.csv
+    data = pd.concat([X, y], axis=1)
+
+    os.makedirs('data', exist_ok=True)
+    data.to_csv('data/raw_data.csv', index=False)
+    # @END save_data
+
+    print(f"Dataset saved with shape: {data.shape}")
+    print(f"Features: {list(feature_names)}")
+    print(f"Target: {y.name}")
+    print("Data acquisition complete!")
+
+    return data
+
+# @END main
+
+if __name__ == "__main__":
+    acquire_data()

benchmark/simple_ml_pipeline_yesworkflow/data_acquisition.dot

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+digraph Workflow {
+rankdir=LR
+fontname=Courier; fontsize=18; labelloc=t
+label=main
+subgraph cluster_workflow_box_outer { label=""; color=black; penwidth=2
+subgraph cluster_workflow_box_inner { label=""; color=white
+node[shape=box style=filled fillcolor="#CCFFCC" peripheries=1 fontname=Courier]
+fetch_raw_data
+process_data
+create_features
+save_data
+edge[fontname=Helvetica]
+process_data -> create_features [label=target_variable]
+fetch_raw_data -> process_data [label=raw_dataset]
+process_data -> create_features [label=feature_matrix]
+create_features -> save_data [label=feature_dataframe]
+create_features -> save_data [label=target_series]
+}}
+subgraph cluster_input_ports_group_outer { label=""; color=white
+subgraph cluster_input_ports_group_inner { label=""; color=white
+node[shape=circle style=filled fillcolor="#FFFFFF" peripheries=1 fontname=Courier width=0.2]
+data_url_input_port [label=""]
+}}
+subgraph cluster_output_ports_group_outer { label=""; color=white
+subgraph cluster_output_ports_group_inner { label=""; color=white
+node[shape=circle style=filled fillcolor="#FFFFFF" peripheries=1 fontname=Courier width=0.2]
+raw_data_output_port [label=""]
+feature_names_output_port [label=""]
+target_variable_output_port [label=""]
+}}
+edge[fontname=Helvetica]
+data_url_input_port -> fetch_raw_data [label=data_url]
+edge[fontname=Helvetica]
+save_data -> raw_data_output_port [label=raw_data]
+process_data -> target_variable_output_port [label=target_variable]
+}