问Spacy文本分类:获取错误消息"'float‘object is not iterable“

EN

def load_data(limit=0, split=0.8):
    """Load the patents data."""
    # Partition off part of the train data for evaluation
    temp=pd.read_csv(excel + 'patents_text_class.csv',header = None)
    new_cols = ['id' , 'class' , 'patent_text']
    temp.columns = new_cols
    print(temp)
    train_data = list(zip(temp["patent_text"], temp["class"]))
    random.shuffle(train_data)
    train_data = train_data[-limit:]
    texts, labels = zip(*train_data)
    cats = [{"A01D": bool(y) , "A01B": operator.not_(bool(y))} for y in labels]
    split = int(len(train_data) * split)
    return (texts[:split], cats[:split]), (texts[split:], cats[split:])

Loaded model 'en_core_web_lg'
Loading patents data...
            id  class                                        patent_text
0         1317      0  Improvement n revolving harrows <div itemprop=...
1         2476      1  Machine for cutting meat and other substances ...
2         2650      0  Improvement in cultivators fob vines <div item...
3         3311      0  Improvement in plows <div itemprop="content" h...
4         4544      0  Improvement in plow-clevises <div itemprop="co...
5         7277      1  Improvement in machines for raking and loading...
6         8721      0  Improvement in shovel-plows <div itemprop="con...
7         8844      0  Improvement in gang-plows <div itemprop="conte...
8         9069      0  Improvement in potato-diggers and stone-gather...
9        10624      0  Improvement in rotary cultivators <div itempro...
10       12057      0  Improvement in hoes <div itemprop="content" ht...
[70000 rows x 3 columns]
Using 10000 examples (8000 training, 2000 evaluation)
Training the model...
LOSS      P       R       F  
Traceback (most recent call last):
  File "process/task_classification.py", line 150, in <module>
    plac.call(main)
  File "/anaconda/lib/python3.6/site-packages/plac_core.py", line 328, in call
    cmd, result = parser.consume(arglist)
  File "/anaconda/lib/python3.6/site-packages/plac_core.py", line 207, in consume
    return cmd, self.func(*(args + varargs + extraopts), **kwargs)
  File "process/task_classification.py", line 78, in main
    losses=losses)
  File "/anaconda/lib/python3.6/site-packages/spacy/language.py", line 405, in update
    gold = GoldParse(doc, **gold)
  File "gold.pyx", line 409, in spacy.gold.GoldParse.__init__
TypeError: 'float' object is not iterable

#!/usr/bin/env python
# coding: utf8
"""Train a convolutional neural network text classifier on the
IMDB dataset, using the TextCategorizer component. The dataset will be loaded
automatically via Thinc's built-in dataset loader. The model is added to
spacy.pipeline, and predictions are available via `doc.cats`. For more details,
see the documentation:
* Training: https://spacy.io/usage/training
Compatible with: spaCy v2.0.0+
"""
from __future__ import unicode_literals, print_function
import plac
import random
from pathlib import Path
import thinc.extra.datasets
import os
import pandas as pd
import operator
import spacy
from spacy.util import minibatch, compounding

root = 'path/to/folder'
output = root + 'output/'
process = root + 'process/'
excel = root + 'excel/'

@plac.annotations(
    model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
    output_dir=("Optional output directory", "option", "o", Path),
    n_texts=("Number of texts to train from", "option", "t", int),
    n_iter=("Number of training iterations", "option", "n", int))
def main(model='en_core_web_lg', output_dir=output, n_iter=5, n_texts=10000):
    if output_dir is not None:
        output_dir = Path(output_dir)
        if not output_dir.exists():
            output_dir.mkdir()

    if model is not None:
        nlp = spacy.load(model)  # load existing spaCy model
        print("Loaded model '%s'" % model)
    else:
        nlp = spacy.blank('en')  # create blank Language class
        print("Created blank 'en' model")

    # add the text classifier to the pipeline if it doesn't exist
    # nlp.create_pipe works for built-ins that are registered with spaCy
    if 'textcat' not in nlp.pipe_names:
        textcat = nlp.create_pipe('textcat')
        nlp.add_pipe(textcat, last=True)
    # otherwise, get it, so we can add labels to it
    else:
        textcat = nlp.get_pipe('textcat')

    # add label to text classifier
    textcat.add_label("A01B")
    textcat.add_label("A01D")
    # load the patents dataset
    print("Loading patents data...")
    (train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=n_texts)
    print("Using {} examples ({} training, {} evaluation)"
          .format(n_texts, len(train_texts), len(dev_texts)))
    train_data = list(zip(train_texts,
                          [{'cats': cats} for cats in train_cats]))

    # get names of other pipes to disable them during training
    other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'textcat']
    with nlp.disable_pipes(*other_pipes):  # only train textcat
        optimizer = nlp.begin_training()
        print("Training the model...")
        print('{:^5}\t{:^5}\t{:^5}\t{:^5}'.format('LOSS', 'P', 'R', 'F'))
        for i in range(n_iter):
            losses = {}
            # batch up the examples using spaCy's minibatch
            batches = minibatch(train_data, size=compounding(4., 32., 1.001))
            for batch in batches:
                texts, annotations = zip(*batch)
                nlp.update(texts, annotations, sgd=optimizer, drop=0.2,
                           losses=losses)
            with textcat.model.use_params(optimizer.averages):
                # evaluate on the dev data split off in load_data()
                scores = evaluate(nlp.tokenizer, textcat, dev_texts, dev_cats)
            print('{0:.3f}\t{1:.3f}\t{2:.3f}\t{3:.3f}'  # print a simple table
                  .format(losses['textcat'], scores['textcat_p'],
                          scores['textcat_r'], scores['textcat_f']))

    # test the trained model
    test_text = "Harvesting"
    doc = nlp(test_text)
    print(test_text, doc.cats)

    test_text = "Plowing"
    doc = nlp(test_text)
    print(test_text, doc.cats)

    if output_dir is not None:
        with nlp.use_params(optimizer.averages):
            nlp.to_disk(output_dir)
        print("Saved model to", output_dir)

        # test the saved model
        print("Loading from", output_dir)
        nlp2 = spacy.load(output_dir)
        doc2 = nlp2(test_text)
        print(test_text, doc2.cats)


def load_data(limit=0, split=0.8):
    """Load the patents data."""
    # Partition off part of the train data for evaluation
    temp=pd.read_csv(excel + 'patents_text_class.csv',header = None)
    new_cols = ['id' , 'class' , 'patent_text']
    temp.columns = new_cols
    train_data = list(zip(temp["patent_text"], temp["class"]))
    random.shuffle(train_data)
    train_data = train_data[-limit:]
    texts, labels = zip(*train_data)
    cats = [{"A01D": bool(y) , "A01B": operator.not_(bool(y))} for y in labels]
    split = int(len(train_data) * split)
    return (texts[:split], cats[:split]), (texts[split:], cats[split:])


def evaluate(tokenizer, textcat, texts, cats):
    docs = (tokenizer(text) for text in texts)
    tp = 0.0   # True positives
    fp = 1e-8  # False positives
    fn = 1e-8  # False negatives
    tn = 0.0   # True negatives
    for i, doc in enumerate(textcat.pipe(docs)):
        gold = cats[i]
        print(i)
        for label, score in doc.cats.items():
            if label not in gold:
                continue
            if score >= 0.5 and gold[label] >= 0.5:
                tp += 1.
            elif score >= 0.5 and gold[label] < 0.5:
                fp += 1.
            elif score < 0.5 and gold[label] < 0.5:
                tn += 1
            elif score < 0.5 and gold[label] >= 0.5:
                fn += 1
    precision = tp / (tp + fp)
    recall = tp / (tp + fn)
    f_score = 2 * (precision * recall) / (precision + recall)
    return {'textcat_p': precision, 'textcat_r': recall, 'textcat_f': f_score}


if __name__ == '__main__':
    plac.call(main)