Graphify / sample_data.py
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COMPLEX_SAMPLE_JSON = """
{
"central_node": "Artificial Intelligence (AI)",
"nodes": [
{
"id": "ml",
"label": "Machine Learning (ML)",
"relationship": "Core Domain",
"subnodes": [
{
"id": "sl",
"label": "Supervised Learning",
"relationship": "Learning Paradigm",
"subnodes": [
{
"id": "sl_prob",
"label": "Probabilistic Models",
"relationship": "Approach Type",
"subnodes": [
{
"id": "nbc",
"label": "Naive Bayes Classifier",
"relationship": "Algorithm Example",
"subnodes": [
{"id": "text_class", "label": "Text Classification", "relationship": "Application"},
{"id": "spam_det", "label": "Spam Detection", "relationship": "Application"}
]
},
{
"id": "lda",
"label": "Linear Discriminant Analysis",
"relationship": "Algorithm Example",
"subnodes": [
{"id": "dim_red_lda", "label": "Dimensionality Reduction", "relationship": "Use Case"},
{"id": "face_recog", "label": "Face Recognition", "relationship": "Use Case"}
]
}
]
},
{
"id": "sl_det",
"label": "Deterministic Models",
"relationship": "Approach Type",
"subnodes": [
{
"id": "svm",
"label": "Support Vector Machines (SVM)",
"relationship": "Algorithm Example",
"subnodes": [
{"id": "image_recog", "label": "Image Recognition", "relationship": "Application"},
{"id": "bioinfo", "label": "Bioinformatics", "relationship": "Application"}
]
},
{
"id": "dt",
"label": "Decision Trees",
"relationship": "Algorithm Example",
"subnodes": [
{"id": "credit_scoring", "label": "Credit Scoring", "relationship": "Application"},
{"id": "medical_diag", "label": "Medical Diagnosis", "relationship": "Application"}
]
}
]
}
]
},
{
"id": "ul",
"label": "Unsupervised Learning",
"relationship": "Learning Paradigm",
"subnodes": [
{
"id": "clus",
"label": "Clustering",
"relationship": "Task Type",
"subnodes": [
{
"id": "kmeans",
"label": "K-Means Clustering",
"relationship": "Algorithm Example",
"subnodes": [
{"id": "customer_seg", "label": "Customer Segmentation", "relationship": "Application"},
{"id": "document_analysis", "label": "Document Analysis", "relationship": "Application"}
]
},
{
"id": "dbscan",
"label": "DBSCAN",
"relationship": "Algorithm Example",
"subnodes": [
{"id": "anomaly_det", "label": "Anomaly Detection", "relationship": "Application"},
{"id": "spatial_data", "label": "Spatial Data Analysis", "relationship": "Application"}
]
}
]
},
{
"id": "dim_red_ul",
"label": "Dimensionality Reduction",
"relationship": "Task Type",
"subnodes": [
{
"id": "pca",
"label": "Principal Component Analysis (PCA)",
"relationship": "Algorithm Example",
"subnodes": [
{"id": "feature_ext", "label": "Feature Extraction", "relationship": "Use Case"},
{"id": "noise_red", "label": "Noise Reduction", "relationship": "Use Case"}
]
},
{
"id": "tsne",
"label": "t-SNE",
"relationship": "Algorithm Example",
"subnodes": [
{"id": "data_viz", "label": "Data Visualization", "relationship": "Application"},
{"id": "genomics", "label": "Genomics", "relationship": "Application"}
]
}
]
}
]
}
]
},
{
"id": "dl",
"label": "Deep Learning (DL)",
"relationship": "Subfield of ML",
"subnodes": [
{
"id": "cnn",
"label": "Convolutional Neural Networks (CNNs)",
"relationship": "Architecture Type",
"subnodes": [
{
"id": "img_class",
"label": "Image Classification",
"relationship": "Primary Use",
"subnodes": [
{
"id": "alexnet",
"label": "AlexNet",
"relationship": "Historic Model",
"subnodes": [
{"id": "imagenet", "label": "ImageNet Challenge", "relationship": "Milestone"},
{"id": "gpu_accel", "label": "GPU Acceleration", "relationship": "Enabling Factor"}
]
},
{
"id": "resnet",
"label": "ResNet",
"relationship": "Advanced Model",
"subnodes": [
{"id": "residual_con", "label": "Residual Connections", "relationship": "Key Feature"},
{"id": "deeper_nets", "label": "Deeper Networks", "relationship": "Benefit"}
]
}
]
},
{
"id": "obj_det_cnn",
"label": "Object Detection",
"relationship": "Primary Use",
"subnodes": [
{
"id": "yolo",
"label": "YOLO (You Only Look Once)",
"relationship": "Real-time Algorithm",
"subnodes": [
{"id": "speed", "label": "High Speed", "relationship": "Advantage"},
{"id": "single_pass", "label": "Single Pass Detection", "relationship": "Mechanism"}
]
},
{
"id": "faster_rcnn",
"label": "Faster R-CNN",
"relationship": "Region-based Algorithm",
"subnodes": [
{"id": "region_props", "label": "Region Proposals", "relationship": "Mechanism"},
{"id": "accuracy", "label": "High Accuracy", "relationship": "Advantage"}
]
}
]
}
]
},
{
"id": "rnn",
"label": "Recurrent Neural Networks (RNNs)",
"relationship": "Architecture Type",
"subnodes": [
{
"id": "seq_data",
"label": "Sequential Data Processing",
"relationship": "Primary Use",
"subnodes": [
{
"id": "nlp_rnn",
"label": "Natural Language Processing (NLP)",
"relationship": "Application Area",
"subnodes": [
{"id": "text_gen_rnn", "label": "Text Generation", "relationship": "Specific Task"},
{"id": "sentiment_rnn", "label": "Sentiment Analysis", "relationship": "Specific Task"}
]
},
{
"id": "speech_rec",
"label": "Speech Recognition",
"relationship": "Application Area",
"subnodes": [
{"id": "voice_assist", "label": "Voice Assistants", "relationship": "Product Example"},
{"id": "transcription", "label": "Audio Transcription", "relationship": "Task"}
]
}
]
},
{
"id": "advanced_rnn",
"label": "Advanced RNN Variants",
"relationship": "Improvements",
"subnodes": [
{
"id": "lstm",
"label": "Long Short-Term Memory (LSTM)",
"relationship": "Variant Type",
"subnodes": [
{"id": "vanishing_grad", "label": "Solves Vanishing Gradients", "relationship": "Benefit"},
{"id": "memory_cells", "label": "Internal Memory Cells", "relationship": "Mechanism"}
]
},
{
"id": "gru",
"label": "Gated Recurrent Unit (GRU)",
"relationship": "Variant Type",
"subnodes": [
{"id": "simpler_than_lstm", "label": "Simpler Architecture", "relationship": "Characteristic"},
{"id": "comparable_perf", "label": "Comparable Performance", "relationship": "Characteristic"}
]
}
]
}
]
}
]
}
]
}
"""