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<header>
<h1 class="title">Module <code>ktrain.text.data</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from .. import utils as U
from ..imports import *
from . import preprocessor as tpp
from . import textutils as TU
MAX_FEATURES = 20000
MAXLEN = 400
def texts_from_folder(
datadir,
classes=None,
max_features=MAX_FEATURES,
maxlen=MAXLEN,
ngram_range=1,
train_test_names=["train", "test"],
preprocess_mode="standard",
encoding=None, # detected automatically
lang=None, # detected automatically
val_pct=0.1,
random_state=None,
verbose=1,
):
"""
```
Returns corpus as sequence of word IDs.
Assumes corpus is in the following folder structure:
├── datadir
│ ├── train
│ │ ├── class0 # folder containing documents of class 0
│ │ ├── class1 # folder containing documents of class 1
│ │ ├── class2 # folder containing documents of class 2
│ │ └── classN # folder containing documents of class N
│ └── test
│ ├── class0 # folder containing documents of class 0
│ ├── class1 # folder containing documents of class 1
│ ├── class2 # folder containing documents of class 2
│ └── classN # folder containing documents of class N
Each subfolder should contain documents in plain text format.
If train and test contain additional subfolders that do not represent
classes, they can be ignored by explicitly listing the subfolders of
interest using the classes argument.
Args:
datadir (str): path to folder
classes (list): list of classes (subfolders to consider).
This is simply supplied as the categories argument
to sklearn's load_files function.
max_features (int): maximum number of unigrams to consider
Note: This is only used for preprocess_mode='standard'.
maxlen (int): maximum length of tokens in document
ngram_range (int): If > 1, will include 2=bigrams, 3=trigrams and bigrams
train_test_names (list): list of strings represnting the subfolder
name for train and validation sets
if test name is missing, <val_pct> of training
will be used for validation
preprocess_mode (str): Either 'standard' (normal tokenization) or one of {'bert', 'distilbert'}
tokenization and preprocessing for use with
BERT/DistilBert text classification model.
encoding (str): character encoding to use. Auto-detected if None
lang (str): language. Auto-detected if None.
val_pct(float): Onlyl used if train_test_names has 1 and not 2 names
random_state(int): If integer is supplied, train/test split is reproducible.
IF None, train/test split will be random
verbose (bool): verbosity
```
"""
# check train_test_names
if len(train_test_names) < 1 or len(train_test_names) > 2:
raise ValueError(
"train_test_names must have 1 or two elements for train and optionally validation"
)
# read in training and test corpora
train_str = train_test_names[0]
train_b = load_files(
os.path.join(datadir, train_str), shuffle=True, categories=classes
)
if len(train_test_names) > 1:
test_str = train_test_names[1]
test_b = load_files(
os.path.join(datadir, test_str), shuffle=False, categories=classes
)
x_train = train_b.data
y_train = train_b.target
x_test = test_b.data
y_test = test_b.target
else:
x_train, x_test, y_train, y_test = train_test_split(
train_b.data, train_b.target, test_size=val_pct, random_state=random_state
)
# decode based on supplied encoding
if encoding is None:
encoding = TU.detect_encoding(x_train)
U.vprint("detected encoding: %s" % (encoding), verbose=verbose)
try:
x_train = [x.decode(encoding) for x in x_train]
x_test = [x.decode(encoding) for x in x_test]
except:
U.vprint(
"Decoding with %s failed 1st attempt - using %s with skips"
% (encoding, encoding),
verbose=verbose,
)
x_train = TU.decode_by_line(x_train, encoding=encoding, verbose=verbose)
x_test = TU.decode_by_line(x_test, encoding=encoding, verbose=verbose)
# detect language
if lang is None:
lang = TU.detect_lang(x_train)
check_unsupported_lang(lang, preprocess_mode)
# return preprocessed the texts
preproc_type = tpp.TEXT_PREPROCESSORS.get(preprocess_mode, None)
if None:
raise ValueError("unsupported preprocess_mode")
preproc = preproc_type(
maxlen,
max_features,
class_names=train_b.target_names,
lang=lang,
ngram_range=ngram_range,
)
trn = preproc.preprocess_train(x_train, y_train, verbose=verbose)
val = preproc.preprocess_test(x_test, y_test, verbose=verbose)
return (trn, val, preproc)
def texts_from_csv(
train_filepath,
text_column,
label_columns=[],
val_filepath=None,
max_features=MAX_FEATURES,
maxlen=MAXLEN,
val_pct=0.1,
ngram_range=1,
preprocess_mode="standard",
encoding=None, # auto-detected
lang=None, # auto-detected
sep=",",
is_regression=False,
random_state=None,
verbose=1,
):
"""
```
Loads text data from CSV or TSV file. Class labels are assumed to be
one of the following formats:
1. one-hot-encoded or multi-hot-encoded arrays representing classes:
Example with label_columns=['positive', 'negative'] and text_column='text':
text|positive|negative
I like this movie.|1|0
I hated this movie.|0|1
Classification will have a single one in each row: [[1,0,0], [0,1,0]]]
Multi-label classification will have one more ones in each row: [[1,1,0], [0,1,1]]
2. labels are in a single column of string or integer values representing classs labels
Example with label_columns=['label'] and text_column='text':
text|label
I like this movie.|positive
I hated this movie.|negative
3. labels are a single column of numerical values for text regression
NOTE: Must supply is_regression=True for labels to be treated as numerical targets
wine_description|wine_price
Exquisite wine!|100
Wine for budget shoppers|8
Args:
train_filepath(str): file path to training CSV
text_column(str): name of column containing the text
label_column(list): list of columns that are to be treated as labels
val_filepath(string): file path to test CSV. If not supplied,
10% of documents in training CSV will be
used for testing/validation.
max_features(int): max num of words to consider in vocabulary
Note: This is only used for preprocess_mode='standard'.
maxlen(int): each document can be of most <maxlen> words. 0 is used as padding ID.
ngram_range(int): size of multi-word phrases to consider
e.g., 2 will consider both 1-word phrases and 2-word phrases
limited by max_features
val_pct(float): Proportion of training to use for validation.
Has no effect if val_filepath is supplied.
preprocess_mode (str): Either 'standard' (normal tokenization) or one of {'bert', 'distilbert'}
tokenization and preprocessing for use with
BERT/DistilBert text classification model.
encoding (str): character encoding to use. Auto-detected if None
lang (str): language. Auto-detected if None.
sep(str): delimiter for CSV (comma is default)
is_regression(bool): If True, integer targets will be treated as numerical targets instead of class IDs
random_state(int): If integer is supplied, train/test split is reproducible.
If None, train/test split will be random
verbose (boolean): verbosity
```
"""
if encoding is None:
with open(train_filepath, "rb") as f:
# encoding = chardet.detect(f.read())['encoding']
# encoding = 'utf-8' if encoding.lower() in ['ascii', 'utf8', 'utf-8'] else encoding
encoding = TU.detect_encoding(f.read())
U.vprint(
"detected encoding: %s (if wrong, set manually)" % (encoding),
verbose=verbose,
)
train_df = pd.read_csv(train_filepath, encoding=encoding, sep=sep)
val_df = (
pd.read_csv(val_filepath, encoding=encoding, sep=sep)
if val_filepath is not None
else None
)
return texts_from_df(
train_df,
text_column,
label_columns=label_columns,
val_df=val_df,
max_features=max_features,
maxlen=maxlen,
val_pct=val_pct,
ngram_range=ngram_range,
preprocess_mode=preprocess_mode,
lang=lang,
is_regression=is_regression,
random_state=random_state,
verbose=verbose,
)
def texts_from_df(
train_df,
text_column,
label_columns=[],
val_df=None,
max_features=MAX_FEATURES,
maxlen=MAXLEN,
val_pct=0.1,
ngram_range=1,
preprocess_mode="standard",
lang=None, # auto-detected
is_regression=False,
random_state=None,
verbose=1,
):
"""
```
Loads text data from Pandas dataframe file. Class labels are assumed to be
one of the following formats:
1. one-hot-encoded or multi-hot-encoded arrays representing classes:
Example with label_columns=['positive', 'negative'] and text_column='text':
text|positive|negative
I like this movie.|1|0
I hated this movie.|0|1
Classification will have a single one in each row: [[1,0,0], [0,1,0]]]
Multi-label classification will have one more ones in each row: [[1,1,0], [0,1,1]]
2. labels are in a single column of string or integer values representing class labels
Example with label_columns=['label'] and text_column='text':
text|label
I like this movie.|positive
I hated this movie.|negative
3. labels are a single column of numerical values for text regression
NOTE: Must supply is_regression=True for integer labels to be treated as numerical targets
wine_description|wine_price
Exquisite wine!|100
Wine for budget shoppers|8
Args:
train_df(dataframe): Pandas dataframe
text_column(str): name of column containing the text
label_columns(list): list of columns that are to be treated as labels
val_df(dataframe): file path to test dataframe. If not supplied,
10% of documents in training df will be
used for testing/validation.
max_features(int): max num of words to consider in vocabulary.
Note: This is only used for preprocess_mode='standard'.
maxlen(int): each document can be of most <maxlen> words. 0 is used as padding ID.
ngram_range(int): size of multi-word phrases to consider
e.g., 2 will consider both 1-word phrases and 2-word phrases
limited by max_features
val_pct(float): Proportion of training to use for validation.
Has no effect if val_filepath is supplied.
preprocess_mode (str): Either 'standard' (normal tokenization) or one of {'bert', 'distilbert'}
tokenization and preprocessing for use with
BERT/DistilBert text classification model.
lang (str): language. Auto-detected if None.
is_regression(bool): If True, integer targets will be treated as numerical targets instead of class IDs
random_state(int): If integer is supplied, train/test split is reproducible.
If None, train/test split will be random
verbose (boolean): verbosity
```
"""
# read in train and test data
train_df = train_df.copy()
train_df[text_column].fillna("fillna", inplace=True)
if val_df is not None:
val_df = val_df.copy()
val_df[text_column].fillna("fillna", inplace=True)
else:
train_df, val_df = train_test_split(
train_df, test_size=val_pct, random_state=random_state
)
# transform labels
ytransdf = U.YTransformDataFrame(label_columns, is_regression=is_regression)
t_df = ytransdf.apply_train(train_df)
v_df = ytransdf.apply_test(val_df)
class_names = ytransdf.get_classes()
new_lab_cols = ytransdf.get_label_columns(squeeze=True)
x_train = t_df[text_column].values
y_train = t_df[new_lab_cols].values
x_test = v_df[text_column].values
y_test = v_df[new_lab_cols].values
# detect language
if lang is None:
lang = TU.detect_lang(x_train)
check_unsupported_lang(lang, preprocess_mode)
# return preprocessed the texts
preproc_type = tpp.TEXT_PREPROCESSORS.get(preprocess_mode, None)
if None:
raise ValueError("unsupported preprocess_mode")
preproc = preproc_type(
maxlen,
max_features,
class_names=class_names,
lang=lang,
ngram_range=ngram_range,
)
trn = preproc.preprocess_train(x_train, y_train, verbose=verbose)
val = preproc.preprocess_test(x_test, y_test, verbose=verbose)
# QUICKFIX for #314
preproc.ytransform.le = ytransdf.le
return (trn, val, preproc)
def texts_from_array(
x_train,
y_train,
x_test=None,
y_test=None,
class_names=[],
max_features=MAX_FEATURES,
maxlen=MAXLEN,
val_pct=0.1,
ngram_range=1,
preprocess_mode="standard",
lang=None, # auto-detected
random_state=None,
verbose=1,
):
"""
```
Loads and preprocesses text data from arrays.
texts_from_array can handle data for both text classification
and text regression. If class_names is empty, a regression task is assumed.
Args:
x_train(list): list of training texts
y_train(list): labels in one of the following forms:
1. list of integers representing classes (class_names is required)
2. list of strings representing classes (class_names is not needed and ignored.)
3. a one or multi hot encoded array representing classes (class_names is required)
4. numerical values for text regresssion (class_names should be left empty)
x_test(list): list of training texts
y_test(list): labels in one of the following forms:
1. list of integers representing classes (class_names is required)
2. list of strings representing classes (class_names is not needed and ignored.)
3. a one or multi hot encoded array representing classes (class_names is required)
4. numerical values for text regresssion (class_names should be left empty)
class_names (list): list of strings representing class labels
shape should be (num_examples,1) or (num_examples,)
max_features(int): max num of words to consider in vocabulary
Note: This is only used for preprocess_mode='standard'.
maxlen(int): each document can be of most <maxlen> words. 0 is used as padding ID.
ngram_range(int): size of multi-word phrases to consider
e.g., 2 will consider both 1-word phrases and 2-word phrases
limited by max_features
val_pct(float): Proportion of training to use for validation.
Has no effect if x_val and y_val is supplied.
preprocess_mode (str): Either 'standard' (normal tokenization) or one of {'bert', 'distilbert'}
tokenization and preprocessing for use with
BERT/DistilBert text classification model.
lang (str): language. Auto-detected if None.
random_state(int): If integer is supplied, train/test split is reproducible.
If None, train/test split will be random.
verbose (boolean): verbosity
```
"""
U.check_array(x_train, y=y_train, X_name="x_train", y_name="y_train")
if x_test is None or y_test is None:
x_train, x_test, y_train, y_test = train_test_split(
x_train, y_train, test_size=val_pct, random_state=random_state
)
else:
U.check_array(x_test, y=y_test, X_name="x_test", y_name="y_test")
# removed as TextPreprocessor now handles this.
# if isinstance(y_train[0], str):
# if not isinstance(y_test[0], str):
# raise ValueError('y_train contains strings, but y_test does not')
# encoder = LabelEncoder()
# encoder.fit(y_train)
# y_train = encoder.transform(y_train)
# y_test = encoder.transform(y_test)
# detect language
if lang is None:
lang = TU.detect_lang(x_train)
check_unsupported_lang(lang, preprocess_mode)
# return preprocessed the texts
preproc_type = tpp.TEXT_PREPROCESSORS.get(preprocess_mode, None)
if None:
raise ValueError("unsupported preprocess_mode")
preproc = preproc_type(
maxlen,
max_features,
class_names=class_names,
lang=lang,
ngram_range=ngram_range,
)
trn = preproc.preprocess_train(x_train, y_train, verbose=verbose)
val = preproc.preprocess_test(x_test, y_test, verbose=verbose)
if not preproc.get_classes() and verbose:
print(
"task: text regression (supply class_names argument if this is supposed to be classification task)"
)
else:
print("task: text classification")
return (trn, val, preproc)
def check_unsupported_lang(lang, preprocess_mode):
"""
```
check for unsupported language (e.g., nospace langs not supported by Jieba)
```
"""
unsupported = (
preprocess_mode == "standard"
and TU.is_nospace_lang(lang)
and not TU.is_chinese(lang)
)
if unsupported:
raise ValueError(
"language %s is currently only supported by the BERT model. " % (lang)
+ 'Please select preprocess_mode="bert"'
)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.text.data.check_unsupported_lang"><code class="name flex">
<span>def <span class="ident">check_unsupported_lang</span></span>(<span>lang, preprocess_mode)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>check for unsupported language (e.g., nospace langs not supported by Jieba)
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def check_unsupported_lang(lang, preprocess_mode):
"""
```
check for unsupported language (e.g., nospace langs not supported by Jieba)
```
"""
unsupported = (
preprocess_mode == "standard"
and TU.is_nospace_lang(lang)
and not TU.is_chinese(lang)
)
if unsupported:
raise ValueError(
"language %s is currently only supported by the BERT model. " % (lang)
+ 'Please select preprocess_mode="bert"'
)</code></pre>
</details>
</dd>
<dt id="ktrain.text.data.texts_from_array"><code class="name flex">
<span>def <span class="ident">texts_from_array</span></span>(<span>x_train, y_train, x_test=None, y_test=None, class_names=[], max_features=20000, maxlen=400, val_pct=0.1, ngram_range=1, preprocess_mode='standard', lang=None, random_state=None, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Loads and preprocesses text data from arrays.
texts_from_array can handle data for both text classification
and text regression. If class_names is empty, a regression task is assumed.
Args:
x_train(list): list of training texts
y_train(list): labels in one of the following forms:
1. list of integers representing classes (class_names is required)
2. list of strings representing classes (class_names is not needed and ignored.)
3. a one or multi hot encoded array representing classes (class_names is required)
4. numerical values for text regresssion (class_names should be left empty)
x_test(list): list of training texts
y_test(list): labels in one of the following forms:
1. list of integers representing classes (class_names is required)
2. list of strings representing classes (class_names is not needed and ignored.)
3. a one or multi hot encoded array representing classes (class_names is required)
4. numerical values for text regresssion (class_names should be left empty)
class_names (list): list of strings representing class labels
shape should be (num_examples,1) or (num_examples,)
max_features(int): max num of words to consider in vocabulary
Note: This is only used for preprocess_mode='standard'.
maxlen(int): each document can be of most <maxlen> words. 0 is used as padding ID.
ngram_range(int): size of multi-word phrases to consider
e.g., 2 will consider both 1-word phrases and 2-word phrases
limited by max_features
val_pct(float): Proportion of training to use for validation.
Has no effect if x_val and y_val is supplied.
preprocess_mode (str): Either 'standard' (normal tokenization) or one of {'bert', 'distilbert'}
tokenization and preprocessing for use with
BERT/DistilBert text classification model.
lang (str): language. Auto-detected if None.
random_state(int): If integer is supplied, train/test split is reproducible.
If None, train/test split will be random.
verbose (boolean): verbosity
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def texts_from_array(
x_train,
y_train,
x_test=None,
y_test=None,
class_names=[],
max_features=MAX_FEATURES,
maxlen=MAXLEN,
val_pct=0.1,
ngram_range=1,
preprocess_mode="standard",
lang=None, # auto-detected
random_state=None,
verbose=1,
):
"""
```
Loads and preprocesses text data from arrays.
texts_from_array can handle data for both text classification
and text regression. If class_names is empty, a regression task is assumed.
Args:
x_train(list): list of training texts
y_train(list): labels in one of the following forms:
1. list of integers representing classes (class_names is required)
2. list of strings representing classes (class_names is not needed and ignored.)
3. a one or multi hot encoded array representing classes (class_names is required)
4. numerical values for text regresssion (class_names should be left empty)
x_test(list): list of training texts
y_test(list): labels in one of the following forms:
1. list of integers representing classes (class_names is required)
2. list of strings representing classes (class_names is not needed and ignored.)
3. a one or multi hot encoded array representing classes (class_names is required)
4. numerical values for text regresssion (class_names should be left empty)
class_names (list): list of strings representing class labels
shape should be (num_examples,1) or (num_examples,)
max_features(int): max num of words to consider in vocabulary
Note: This is only used for preprocess_mode='standard'.
maxlen(int): each document can be of most <maxlen> words. 0 is used as padding ID.
ngram_range(int): size of multi-word phrases to consider
e.g., 2 will consider both 1-word phrases and 2-word phrases
limited by max_features
val_pct(float): Proportion of training to use for validation.
Has no effect if x_val and y_val is supplied.
preprocess_mode (str): Either 'standard' (normal tokenization) or one of {'bert', 'distilbert'}
tokenization and preprocessing for use with
BERT/DistilBert text classification model.
lang (str): language. Auto-detected if None.
random_state(int): If integer is supplied, train/test split is reproducible.
If None, train/test split will be random.
verbose (boolean): verbosity
```
"""
U.check_array(x_train, y=y_train, X_name="x_train", y_name="y_train")
if x_test is None or y_test is None:
x_train, x_test, y_train, y_test = train_test_split(
x_train, y_train, test_size=val_pct, random_state=random_state
)
else:
U.check_array(x_test, y=y_test, X_name="x_test", y_name="y_test")
# removed as TextPreprocessor now handles this.
# if isinstance(y_train[0], str):
# if not isinstance(y_test[0], str):
# raise ValueError('y_train contains strings, but y_test does not')
# encoder = LabelEncoder()
# encoder.fit(y_train)
# y_train = encoder.transform(y_train)
# y_test = encoder.transform(y_test)
# detect language
if lang is None:
lang = TU.detect_lang(x_train)
check_unsupported_lang(lang, preprocess_mode)
# return preprocessed the texts
preproc_type = tpp.TEXT_PREPROCESSORS.get(preprocess_mode, None)
if None:
raise ValueError("unsupported preprocess_mode")
preproc = preproc_type(
maxlen,
max_features,
class_names=class_names,
lang=lang,
ngram_range=ngram_range,
)
trn = preproc.preprocess_train(x_train, y_train, verbose=verbose)
val = preproc.preprocess_test(x_test, y_test, verbose=verbose)
if not preproc.get_classes() and verbose:
print(
"task: text regression (supply class_names argument if this is supposed to be classification task)"
)
else:
print("task: text classification")
return (trn, val, preproc)</code></pre>
</details>
</dd>
<dt id="ktrain.text.data.texts_from_csv"><code class="name flex">
<span>def <span class="ident">texts_from_csv</span></span>(<span>train_filepath, text_column, label_columns=[], val_filepath=None, max_features=20000, maxlen=400, val_pct=0.1, ngram_range=1, preprocess_mode='standard', encoding=None, lang=None, sep=',', is_regression=False, random_state=None, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Loads text data from CSV or TSV file. Class labels are assumed to be
one of the following formats:
1. one-hot-encoded or multi-hot-encoded arrays representing classes:
Example with label_columns=['positive', 'negative'] and text_column='text':
text|positive|negative
I like this movie.|1|0
I hated this movie.|0|1
Classification will have a single one in each row: [[1,0,0], [0,1,0]]]
Multi-label classification will have one more ones in each row: [[1,1,0], [0,1,1]]
2. labels are in a single column of string or integer values representing classs labels
Example with label_columns=['label'] and text_column='text':
text|label
I like this movie.|positive
I hated this movie.|negative
3. labels are a single column of numerical values for text regression
NOTE: Must supply is_regression=True for labels to be treated as numerical targets
wine_description|wine_price
Exquisite wine!|100
Wine for budget shoppers|8
Args:
train_filepath(str): file path to training CSV
text_column(str): name of column containing the text
label_column(list): list of columns that are to be treated as labels
val_filepath(string): file path to test CSV. If not supplied,
10% of documents in training CSV will be
used for testing/validation.
max_features(int): max num of words to consider in vocabulary
Note: This is only used for preprocess_mode='standard'.
maxlen(int): each document can be of most <maxlen> words. 0 is used as padding ID.
ngram_range(int): size of multi-word phrases to consider
e.g., 2 will consider both 1-word phrases and 2-word phrases
limited by max_features
val_pct(float): Proportion of training to use for validation.
Has no effect if val_filepath is supplied.
preprocess_mode (str): Either 'standard' (normal tokenization) or one of {'bert', 'distilbert'}
tokenization and preprocessing for use with
BERT/DistilBert text classification model.
encoding (str): character encoding to use. Auto-detected if None
lang (str): language. Auto-detected if None.
sep(str): delimiter for CSV (comma is default)
is_regression(bool): If True, integer targets will be treated as numerical targets instead of class IDs
random_state(int): If integer is supplied, train/test split is reproducible.
If None, train/test split will be random
verbose (boolean): verbosity
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def texts_from_csv(
train_filepath,
text_column,
label_columns=[],
val_filepath=None,
max_features=MAX_FEATURES,
maxlen=MAXLEN,
val_pct=0.1,
ngram_range=1,
preprocess_mode="standard",
encoding=None, # auto-detected
lang=None, # auto-detected
sep=",",
is_regression=False,
random_state=None,
verbose=1,
):
"""
```
Loads text data from CSV or TSV file. Class labels are assumed to be
one of the following formats:
1. one-hot-encoded or multi-hot-encoded arrays representing classes:
Example with label_columns=['positive', 'negative'] and text_column='text':
text|positive|negative
I like this movie.|1|0
I hated this movie.|0|1
Classification will have a single one in each row: [[1,0,0], [0,1,0]]]
Multi-label classification will have one more ones in each row: [[1,1,0], [0,1,1]]
2. labels are in a single column of string or integer values representing classs labels
Example with label_columns=['label'] and text_column='text':
text|label
I like this movie.|positive
I hated this movie.|negative
3. labels are a single column of numerical values for text regression
NOTE: Must supply is_regression=True for labels to be treated as numerical targets
wine_description|wine_price
Exquisite wine!|100
Wine for budget shoppers|8
Args:
train_filepath(str): file path to training CSV
text_column(str): name of column containing the text
label_column(list): list of columns that are to be treated as labels
val_filepath(string): file path to test CSV. If not supplied,
10% of documents in training CSV will be
used for testing/validation.
max_features(int): max num of words to consider in vocabulary
Note: This is only used for preprocess_mode='standard'.
maxlen(int): each document can be of most <maxlen> words. 0 is used as padding ID.
ngram_range(int): size of multi-word phrases to consider
e.g., 2 will consider both 1-word phrases and 2-word phrases
limited by max_features
val_pct(float): Proportion of training to use for validation.
Has no effect if val_filepath is supplied.
preprocess_mode (str): Either 'standard' (normal tokenization) or one of {'bert', 'distilbert'}
tokenization and preprocessing for use with
BERT/DistilBert text classification model.
encoding (str): character encoding to use. Auto-detected if None
lang (str): language. Auto-detected if None.
sep(str): delimiter for CSV (comma is default)
is_regression(bool): If True, integer targets will be treated as numerical targets instead of class IDs
random_state(int): If integer is supplied, train/test split is reproducible.
If None, train/test split will be random
verbose (boolean): verbosity
```
"""
if encoding is None:
with open(train_filepath, "rb") as f:
# encoding = chardet.detect(f.read())['encoding']
# encoding = 'utf-8' if encoding.lower() in ['ascii', 'utf8', 'utf-8'] else encoding
encoding = TU.detect_encoding(f.read())
U.vprint(
"detected encoding: %s (if wrong, set manually)" % (encoding),
verbose=verbose,
)
train_df = pd.read_csv(train_filepath, encoding=encoding, sep=sep)
val_df = (
pd.read_csv(val_filepath, encoding=encoding, sep=sep)
if val_filepath is not None
else None
)
return texts_from_df(
train_df,
text_column,
label_columns=label_columns,
val_df=val_df,
max_features=max_features,
maxlen=maxlen,
val_pct=val_pct,
ngram_range=ngram_range,
preprocess_mode=preprocess_mode,
lang=lang,
is_regression=is_regression,
random_state=random_state,
verbose=verbose,
)</code></pre>
</details>
</dd>
<dt id="ktrain.text.data.texts_from_df"><code class="name flex">
<span>def <span class="ident">texts_from_df</span></span>(<span>train_df, text_column, label_columns=[], val_df=None, max_features=20000, maxlen=400, val_pct=0.1, ngram_range=1, preprocess_mode='standard', lang=None, is_regression=False, random_state=None, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Loads text data from Pandas dataframe file. Class labels are assumed to be
one of the following formats:
1. one-hot-encoded or multi-hot-encoded arrays representing classes:
Example with label_columns=['positive', 'negative'] and text_column='text':
text|positive|negative
I like this movie.|1|0
I hated this movie.|0|1
Classification will have a single one in each row: [[1,0,0], [0,1,0]]]
Multi-label classification will have one more ones in each row: [[1,1,0], [0,1,1]]
2. labels are in a single column of string or integer values representing class labels
Example with label_columns=['label'] and text_column='text':
text|label
I like this movie.|positive
I hated this movie.|negative
3. labels are a single column of numerical values for text regression
NOTE: Must supply is_regression=True for integer labels to be treated as numerical targets
wine_description|wine_price
Exquisite wine!|100
Wine for budget shoppers|8
Args:
train_df(dataframe): Pandas dataframe
text_column(str): name of column containing the text
label_columns(list): list of columns that are to be treated as labels
val_df(dataframe): file path to test dataframe. If not supplied,
10% of documents in training df will be
used for testing/validation.
max_features(int): max num of words to consider in vocabulary.
Note: This is only used for preprocess_mode='standard'.
maxlen(int): each document can be of most <maxlen> words. 0 is used as padding ID.
ngram_range(int): size of multi-word phrases to consider
e.g., 2 will consider both 1-word phrases and 2-word phrases
limited by max_features
val_pct(float): Proportion of training to use for validation.
Has no effect if val_filepath is supplied.
preprocess_mode (str): Either 'standard' (normal tokenization) or one of {'bert', 'distilbert'}
tokenization and preprocessing for use with
BERT/DistilBert text classification model.
lang (str): language. Auto-detected if None.
is_regression(bool): If True, integer targets will be treated as numerical targets instead of class IDs
random_state(int): If integer is supplied, train/test split is reproducible.
If None, train/test split will be random
verbose (boolean): verbosity
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def texts_from_df(
train_df,
text_column,
label_columns=[],
val_df=None,
max_features=MAX_FEATURES,
maxlen=MAXLEN,
val_pct=0.1,
ngram_range=1,
preprocess_mode="standard",
lang=None, # auto-detected
is_regression=False,
random_state=None,
verbose=1,
):
"""
```
Loads text data from Pandas dataframe file. Class labels are assumed to be
one of the following formats:
1. one-hot-encoded or multi-hot-encoded arrays representing classes:
Example with label_columns=['positive', 'negative'] and text_column='text':
text|positive|negative
I like this movie.|1|0
I hated this movie.|0|1
Classification will have a single one in each row: [[1,0,0], [0,1,0]]]
Multi-label classification will have one more ones in each row: [[1,1,0], [0,1,1]]
2. labels are in a single column of string or integer values representing class labels
Example with label_columns=['label'] and text_column='text':
text|label
I like this movie.|positive
I hated this movie.|negative
3. labels are a single column of numerical values for text regression
NOTE: Must supply is_regression=True for integer labels to be treated as numerical targets
wine_description|wine_price
Exquisite wine!|100
Wine for budget shoppers|8
Args:
train_df(dataframe): Pandas dataframe
text_column(str): name of column containing the text
label_columns(list): list of columns that are to be treated as labels
val_df(dataframe): file path to test dataframe. If not supplied,
10% of documents in training df will be
used for testing/validation.
max_features(int): max num of words to consider in vocabulary.
Note: This is only used for preprocess_mode='standard'.
maxlen(int): each document can be of most <maxlen> words. 0 is used as padding ID.
ngram_range(int): size of multi-word phrases to consider
e.g., 2 will consider both 1-word phrases and 2-word phrases
limited by max_features
val_pct(float): Proportion of training to use for validation.
Has no effect if val_filepath is supplied.
preprocess_mode (str): Either 'standard' (normal tokenization) or one of {'bert', 'distilbert'}
tokenization and preprocessing for use with
BERT/DistilBert text classification model.
lang (str): language. Auto-detected if None.
is_regression(bool): If True, integer targets will be treated as numerical targets instead of class IDs
random_state(int): If integer is supplied, train/test split is reproducible.
If None, train/test split will be random
verbose (boolean): verbosity
```
"""
# read in train and test data
train_df = train_df.copy()
train_df[text_column].fillna("fillna", inplace=True)
if val_df is not None:
val_df = val_df.copy()
val_df[text_column].fillna("fillna", inplace=True)
else:
train_df, val_df = train_test_split(
train_df, test_size=val_pct, random_state=random_state
)
# transform labels
ytransdf = U.YTransformDataFrame(label_columns, is_regression=is_regression)
t_df = ytransdf.apply_train(train_df)
v_df = ytransdf.apply_test(val_df)
class_names = ytransdf.get_classes()
new_lab_cols = ytransdf.get_label_columns(squeeze=True)
x_train = t_df[text_column].values
y_train = t_df[new_lab_cols].values
x_test = v_df[text_column].values
y_test = v_df[new_lab_cols].values
# detect language
if lang is None:
lang = TU.detect_lang(x_train)
check_unsupported_lang(lang, preprocess_mode)
# return preprocessed the texts
preproc_type = tpp.TEXT_PREPROCESSORS.get(preprocess_mode, None)
if None:
raise ValueError("unsupported preprocess_mode")
preproc = preproc_type(
maxlen,
max_features,
class_names=class_names,
lang=lang,
ngram_range=ngram_range,
)
trn = preproc.preprocess_train(x_train, y_train, verbose=verbose)
val = preproc.preprocess_test(x_test, y_test, verbose=verbose)
# QUICKFIX for #314
preproc.ytransform.le = ytransdf.le
return (trn, val, preproc)</code></pre>
</details>
</dd>
<dt id="ktrain.text.data.texts_from_folder"><code class="name flex">
<span>def <span class="ident">texts_from_folder</span></span>(<span>datadir, classes=None, max_features=20000, maxlen=400, ngram_range=1, train_test_names=['train', 'test'], preprocess_mode='standard', encoding=None, lang=None, val_pct=0.1, random_state=None, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Returns corpus as sequence of word IDs.
Assumes corpus is in the following folder structure:
├── datadir
│ ├── train
│ │ ├── class0 # folder containing documents of class 0
│ │ ├── class1 # folder containing documents of class 1
│ │ ├── class2 # folder containing documents of class 2
│ │ └── classN # folder containing documents of class N
│ └── test
│ ├── class0 # folder containing documents of class 0
│ ├── class1 # folder containing documents of class 1
│ ├── class2 # folder containing documents of class 2
│ └── classN # folder containing documents of class N
Each subfolder should contain documents in plain text format.
If train and test contain additional subfolders that do not represent
classes, they can be ignored by explicitly listing the subfolders of
interest using the classes argument.
Args:
datadir (str): path to folder
classes (list): list of classes (subfolders to consider).
This is simply supplied as the categories argument
to sklearn's load_files function.
max_features (int): maximum number of unigrams to consider
Note: This is only used for preprocess_mode='standard'.
maxlen (int): maximum length of tokens in document
ngram_range (int): If > 1, will include 2=bigrams, 3=trigrams and bigrams