TechFlow

Installation

pip install git+https://github.com/lee-ju/techflow.git

Usage

IPC Code Network

from techflow.nx_tech import nx_preps, nx_utils
import pandas as pd

## Read dataset
sample_ipcs = pd.read_csv('sample_dataset/sample_ipc.csv')
ipcs = sample_ipcs['all_ipcs'].tolist()

## Preprocessing
ipcs_preps = nx_preps(ipcs)
ipcs_df = ipcs_preps.edges(obj='ipcs', num_slice=4, spliter='||')

## Network Centrality
ipcs_utils = nx_utils(ipcs_df, direct=False)
ipcs_central = ipcs_utils.nx_centrality(top_k=3)
print(ipcs_central)

## Visualizing
ipcs_G = ipcs_utils.nx_viz(fs=[5, 5], with_labels=True,
                           font_size=10, font_color='blue',
                           node_size=100, node_color='red', seed=15)

[Out]:

100%|██████████| 20/20 [00:00<00:00, 63453.92it/s]
        Degree  Closeness  Betweenness  Centrality
id                                                
G06F  0.375000   0.421053     0.721014    1.517067
G02B  0.250000   0.296296     0.369565    0.915862
H04Q  0.083333   0.352941     0.391304    0.827579

Citation Network

from techflow.nx_tech import nx_preps, nx_utils
import pandas as pd

## Read dataset
sample_forws = pd.read_csv('sample_dataset/sample_forw.csv')
x = sample_forws['Reg_id'].tolist()
apps = sample_forws['App_id'].tolist()
forws = sample_forws['Forw_in_id'].tolist()

## Preprocessing
forws_preps = nx_preps(x=x, apps=apps, forws=forws)
forws_df = forws_preps.edges(obj='forws', num_slice=0, spliter='||')

## Network Centrality
forws_utils = nx_utils(forws_df, direct=False)
forws_central = forws_utils.nx_centrality(top_k=5)
print(forws_central)

## Visualizing
forws_G = forws_utils.nx_viz(fs=[5, 5], with_labels=True,
                             font_size=10, font_color='black',
                             node_size=100, node_color='blue', seed=15)

[Out]: 

100%|██████████| 10/10 [00:00<00:00, 23484.34it/s]
             Degree  Closeness  Betweenness  Centrality
id                                                     
US2345678  0.444444   0.363636     0.236111    1.044192
US5678901  0.444444   0.285714     0.152778    0.882937
US6789012  0.555556   0.210526     0.000000    0.766082
US4567890  0.333333   0.418301     0.000000    0.751634
US1234567  0.333333   0.250000     0.111111    0.694444

Patents with Indirect Connection (see Paper)

from techflow.pic import pic_preps, pic_utils
import pandas as pd

## Read dataset
sample_pic = pd.read_csv('sample_dataset/sample_pic.csv')
apps = sample_pic['App_id'].tolist()
regs = sample_pic['Reg_id'].tolist()
regs_date = sample_pic['Reg_date'].tolist()
forws = sample_pic['Forw_in_id'].tolist()
texts = sample_pic['Text'].tolist()

## Preprocessing: CAM
pp = pic_preps(apps=apps, regs=regs, regs_date=regs_date,
               forws=forws, texts=texts)
repo = pp.get_repo(num_slice=0)
from_cam, to_cam = pp.get_cam(num_slice=0, spliter='||')

## Preprocessing: SAM
#ptrain_path = '.../GoogleNews-vectors-negative300.bin.gz'#Example for ptrain
from_sam, to_sam = pp.get_sam(
    max_features=100, min_sim=0.6,
    use_ptrain=False, use_weight=False, ptrain_path=None)

## PIC-Explorer
pu = pic_utils(from_cam, to_cam, from_sam, to_sam, repo, direct=True)
pic_E, pic_L = pu.explorer(max_date=20)
pic = {'P_E': pic_E, 'P_L': pic_L}
df_pic = pd.DataFrame(pic)
print(df_pic)

## PIC-Visualization
CS_net = pu.cs_net(pic_E, pic_L, fs=[3, 3], with_labels=True,
                   node_size=300, font_size=12, seed=10)

[Out]:

100%|██████████| 1/1 [00:00<00:00, 11366.68it/s]
  P_E P_L
0   C   A

Parameters

techflow.nx_tech

• nx_preps constructor:

  1. x: The data for social network analysis. On the input will always be list.
  2. apps: Applicant Number. (default: None)
  3. forws: Forward citation list. (default: None)

• nx_preps.edges constructor:

  1. obj: 'ipcs' for IPC code network, 'forws' for citation network. (default: 'ipcs')
  2. num_slice: An argument to how much to truncate the code from behind. (default: 4)
  3. spliter: An arguments to break code. (default: '||')

• nx_utils constructor:

  1. df: Dataframe of edgelist.
  2. direct: Boolean controlling the DiGraph. (default: True)

• nx_utils.viz constructor:

  1. fs: List of figsize=[horizontal_size, vertical_size]. (default: [10, 10])
  2. with_labels: Boolean controlling the use of node labels. (default: True)
  3. node_size: Size of nodes. (default: 100)
  4. node_color: Node color. (default: 'red')
  5. font_size: Size of labels. (default: 12)
  6. font_color: Node label. (default: 'black')
  7. seed: Seed for random visualization. (default: 10)

• nx_utils.centrality constructor:

  1. top_k: Return centrality by top_k. (default: 10)

techflow.nlp_tech

• nlp_preps constructor:

  1. x: Texts to be DTM.

• nlp_preps.dtmx constructor:

  1. max_features: If not None, build a vocabulary that only consider the top max_features ordered by term frequency across the corpus. (default: 100)
  2. use_ptrain: True if using the pre-trained word embedding model, False if using only tf-idf. (default: True)
  3. use_weight: True if embedding values are used as weights, False otherwise. Select only when use_ptrain is True. (default: True)
  4. ptrain_path: Path for pre-trained word embedding model. (default: None)

techflow.pic (see: PIC)

• pic_preps constructor:

  1. apps: Applicant id.
  2. regs: Registration id.
  3. regs_date: Registration dates.
  4. forws: Forward citation patents.
  5. texts: Text of documents. (default: None)

• pic_preps.get_cam constructor:

  1. num_slice: An argument to how much to truncate the code from behind. (default: 4)
  2. spliter: An arguments to break code. (default: '||')

• pic_preps.get_sam constructor:

  1. max_features: If not None, build a vocabulary that only consider the top max_features ordered by term frequency across the corpus. (default: 100)
  2. use_ptrain: True if using the pre-trained word embedding model, False if using only tf-idf. (default: True)
  3. use_weight: True if embedding values are used as weights, False otherwise. Select only when use_ptrain is True. (default: True)
  4. ptrain_path: Path for pre-trained word embedding model. (default: None)
  5. min_sim: Minimum value of similarity (threshold value on PIC). (default: 0.5)

• pic_preps.get_repo constructor:

  1. num_slice: An argument to how much to truncate the code from behind. (default: 4)

• pic_utils constructor:

  1. from_cam: In-node lists of Citation Adjacency Matrix.
  2. to_cam: Out-node lists of Citation Adjacency Matrix.
  3. from_sam: In-node lists of Similarity Adjacency Matrix.
  4. to_sam: Out-node lists of Similarity Adjacency Matrix.
  5. repo: Dictionary of apps and apps_date.
  6. direct: Boolean controlling the DiGraph. (default: True)

• pic_utils.explorer constructor:

  1. max_date: The maximum value of the time difference between the filing of two patents. (default: 20)

• pic_utils.cs_net constructor:

  1. pic_E: Output of pic_utils.explorer (CS-Net on PIC).
  2. fs: List of figsize=[horizontal_size, vertical_size]. (default: [10, 10])
  3. with_labels: Boolean controlling the use of node labels. (default: True)
  4. node_size: Size of nodes. (default: 100)
  5. font_size: Size of labels. (default: 12)
  6. seed: Seed for random visualization. (default: 10)

TODO

• IPC Network
• Citation Network
• NLP
• PIC
• ...