add loading csv data to database.

src/scrape.py

@@ -4,7 +4,7 @@ import requests
 from pathlib import Path
 import click
 from tqdm import tqdm
-from data import data_dir
+from data import data_dir, connect
 from lxml import etree
 import pandas as pd
 
@@ -12,6 +12,29 @@ import pandas as pd
 def cli():
     ...
 
+@cli.command()
+@click.option('--directory', type=Path, default=data_dir())
+@click.option('--database', type=Path, default=data_dir() / "stories.duckdb")
+def load(directory, database):
+    stories = directory / "stories.csv"
+    related = directory / "related.csv"
+    db = connect()
+
+    db.sql(f"""
+        CREATE TABLE stories AS
+        SELECT 
+            * 
+        FROM read_csv_auto('{stories}')
+    """)
+
+    db.sql(f"""
+        CREATE TABLE related_stories AS
+        SELECT 
+            * 
+        FROM read_csv_auto('{related}')
+    """)
+    db.close()
+
 @cli.command()
 @click.option('-o', 'output_dir', type=Path, default=data_dir() / "memeorandum")
 def download(output_dir):
@@ -20,6 +43,7 @@ def download(output_dir):
     end = date.today()
     dates = []
     while cur <= end:
+        if not (output_dir / f"{cur.strftime('%y-%m-%d')}.html").exists():
             dates.append(cur)
         cur = cur + day
     date_iter = tqdm(dates, postfix="test")
@@ -51,6 +75,9 @@ def parse(directory, output_dir):
         # tree = etree.parse(str(page), parser)
         tree = etree.parse(str(page), parser)
         root = tree.getroot()
+        if not root:
+            print(f"error opening {page}")
+            continue
         items = root.xpath("//div[contains(@class, 'item')]")
 
         for item in items:
@@ -64,8 +91,11 @@ def parse(directory, output_dir):
             else:
                 author = ''
             out['author'] = author
+            try:
                 url = citation[0].getchildren()[0].get('href')
                 publisher = citation[0].getchildren()[0].text
+            except IndexError as e:
+                print(f"error with citation url: {page}")
             out['publisher'] = publisher
             out['publisher_url'] = url
             title = item.xpath('.//strong/a')[0].text

src/word.py

@@ -14,20 +14,21 @@ def train():
     table = from_db(Data.Titles)
     n_classes = 10
     tokenizer = AutoTokenizer.from_pretrained("roberta-base")
-
-    # create tokens, padding to max width 
-    tokens = tokenizer(table['title'].apply(str).to_list(), add_special_tokens = True, truncation = True, padding = "max_length", return_attention_mask = True, return_tensors = "pt")
-    pred_y = outputs[:, 0, :]
-
     model = RobertaModel.from_pretrained("roberta-base")
-    pred_y = model(**inputs)
+
+    def get_embeddings(titles):
+        # create tokens, padding to max width 
+        tokens = tokenizer(titles, add_special_tokens = True, truncation = True, padding = "max_length", return_attention_mask = True, return_tensors = "pt")
         outputs = model(**tokens)
+        return outputs.last_hidden_state[:, 0, :]
+
+    titles = table['title'].apply(str).to_list()[:10]
+    get_embeddings(titles)
 
     # linear = torch.nn.Linear(model.config.hidden_size, n_classes)
     # act = torch.nn.Sigmoid()
 
     # model = Model()
-    pred_y.last_hidden_state[:, 0, :].shape
     classes = act(linear(pred_y.last_hidden_state[:, 0, :])).detach()
 
 @cli.command()