Merge pull request #6 from csinva/divyanshuaggarwal-da_min_ngram

Min frequency ngrams from @divyanshuaggarwal

Author: csinva

docs/auggam/auggam.html

@@ -29,9 +29,14 @@ <h1 class="title">Module <code>imodelsx.auggam.embed</code></h1>
 <pre><code class="python">from transformers import BertModel, DistilBertModel
 from transformers import AutoModelForCausalLM
 from os.path import join as oj
+from datasets import Dataset
+from tqdm import tqdm
 import torch
+import numpy as np
+from torch.utils.data import DataLoader
 import imodelsx.util
 
+
 def get_model(checkpoint):
     if &#39;distilbert&#39; in checkpoint.lower():
         model = DistilBertModel.from_pretrained(checkpoint)
@@ -79,7 +84,8 @@ <h1 class="title">Module <code>imodelsx.auggam.embed</code></h1>
     checkpoint: str,
     dataset_key_text: str = None,
     layer: str = &#39;last_hidden_state&#39;,
-    padding: bool = True,
+    padding: str = &#34;max_length&#34;,
+    batch_size: int = 8,
     parsing: str = &#39;&#39;,
     nlp_chunks=None,
     all_ngrams: bool = False,
@@ -134,27 +140,57 @@ <h1 class="title">Module <code>imodelsx.auggam.embed</code></h1>
             tokenizer_embeddings.pad_token = tokenizer_embeddings.eos_token
         tokens = tokenizer_embeddings(seqs, padding=padding,
                                       truncation=True, return_tensors=&#34;pt&#34;)
-        tokens = tokens.to(model.device)
-        output = model(**tokens)
-        if layer == &#39;pooler_output&#39;:
-            embs = output[&#39;pooler_output&#39;].cpu().detach().numpy()
-        elif layer == &#39;last_hidden_state_mean&#39; or layer == &#39;last_hidden_state&#39;:
-            embs = output[&#39;last_hidden_state&#39;].cpu().detach().numpy()
-            embs = embs.mean(axis=1)
+
+        embs = []
+
+        ds = Dataset.from_dict(tokens).with_format(&#34;torch&#34;)
+
+        for batch in DataLoader(ds, batch_size=batch_size, shuffle=False):
+            batch = {k: v.to(model.device) for k, v in batch.items()}
+
+            with torch.no_grad():
+                output = model(**batch)
+            torch.cuda.empty_cache()
+
+            if layer == &#39;pooler_output&#39;:
+                emb = output[&#39;pooler_output&#39;].cpu().detach().numpy()
+            elif layer == &#39;last_hidden_state_mean&#39; or layer == &#39;last_hidden_state&#39;:
+                emb = output[&#39;last_hidden_state&#39;].cpu().detach().numpy()
+                emb = emb.mean(axis=1)
+
+            embs.append(emb)
+
+        embs = np.concatenate(embs)
+
     elif &#39;gpt&#39; in checkpoint.lower():
         tokens = preprocess_gpt_token_batch(seqs, tokenizer_embeddings)
-        tokens = tokens.to(model.device)
-        output = model(**tokens)
-
-        # tuple of (layer x (batch_size, seq_len, hidden_size))
-        h = output[&#39;hidden_states&#39;]
-        # (batch_size, seq_len, hidden_size)
-        embs = h[0].cpu().detach().numpy()
-        embs = embs.mean(axis=1)  # (batch_size, hidden_size)
+
+        embs = []
+
+        ds = Dataset.from_dict(tokens).with_format(&#34;torch&#34;)
+
+        for batch in DataLoader(ds, batch_size=batch_size, shuffle=False):
+            batch = {k: v.to(model.device) for k, v in batch.items()}
+
+            with torch.no_grad():
+                output = model(**batch)
+            torch.cuda.empty_cache()
+
+            # tuple of (layer x (batch_size, seq_len, hidden_size))
+            h = output[&#39;hidden_states&#39;]
+            # (batch_size, seq_len, hidden_size)
+            emb = h[0].cpu().detach().numpy()
+            emb = emb.mean(axis=1)  # (batch_size, hidden_size)
+
+            embs.append(emb)
+
+        embs = np.concatenate(embs)
+
     elif checkpoint.startswith(&#39;hkunlp/instructor&#39;):
         if instructor_prompt is None:
             instructor_prompt = &#34;Represent the short phrase for sentiment classification: &#34;
-        embs = model.encode([[instructor_prompt, x_i] for x_i in seqs], batch_size=32)
+        embs = model.encode([[instructor_prompt, x_i]
+                            for x_i in seqs], batch_size=batch_size)
 
     # sum over the embeddings
     embs = embs.sum(axis=0).reshape(1, -1)
@@ -172,7 +208,7 @@ <h1 class="title">Module <code>imodelsx.auggam.embed</code></h1>
 <h2 class="section-title" id="header-functions">Functions</h2>
 <dl>
 <dt id="imodelsx.auggam.embed.embed_and_sum_function"><code class="name flex">
-<span>def <span class="ident">embed_and_sum_function</span></span>(<span>example, model, ngrams: int, tokenizer_embeddings, tokenizer_ngrams, checkpoint: str, dataset_key_text: str = None, layer: str = 'last_hidden_state', padding: bool = True, parsing: str = '', nlp_chunks=None, all_ngrams: bool = False, fit_with_ngram_decomposition: bool = True, instructor_prompt: str = None)</span>
+<span>def <span class="ident">embed_and_sum_function</span></span>(<span>example, model, ngrams: int, tokenizer_embeddings, tokenizer_ngrams, checkpoint: str, dataset_key_text: str = None, layer: str = 'last_hidden_state', padding: str = 'max_length', batch_size: int = 8, parsing: str = '', nlp_chunks=None, all_ngrams: bool = False, fit_with_ngram_decomposition: bool = True, instructor_prompt: str = None)</span>
 </code></dt>
 <dd>
 <div class="desc"><p>Get summed embeddings for a single example</p>
@@ -206,7 +242,8 @@ <h2 id="params">Params</h2>
     checkpoint: str,
     dataset_key_text: str = None,
     layer: str = &#39;last_hidden_state&#39;,
-    padding: bool = True,
+    padding: str = &#34;max_length&#34;,
+    batch_size: int = 8,
     parsing: str = &#39;&#39;,
     nlp_chunks=None,
     all_ngrams: bool = False,
@@ -261,27 +298,57 @@ <h2 id="params">Params</h2>
             tokenizer_embeddings.pad_token = tokenizer_embeddings.eos_token
         tokens = tokenizer_embeddings(seqs, padding=padding,
                                       truncation=True, return_tensors=&#34;pt&#34;)
-        tokens = tokens.to(model.device)
-        output = model(**tokens)
-        if layer == &#39;pooler_output&#39;:
-            embs = output[&#39;pooler_output&#39;].cpu().detach().numpy()
-        elif layer == &#39;last_hidden_state_mean&#39; or layer == &#39;last_hidden_state&#39;:
-            embs = output[&#39;last_hidden_state&#39;].cpu().detach().numpy()
-            embs = embs.mean(axis=1)
+
+        embs = []
+
+        ds = Dataset.from_dict(tokens).with_format(&#34;torch&#34;)
+
+        for batch in DataLoader(ds, batch_size=batch_size, shuffle=False):
+            batch = {k: v.to(model.device) for k, v in batch.items()}
+
+            with torch.no_grad():
+                output = model(**batch)
+            torch.cuda.empty_cache()
+
+            if layer == &#39;pooler_output&#39;:
+                emb = output[&#39;pooler_output&#39;].cpu().detach().numpy()
+            elif layer == &#39;last_hidden_state_mean&#39; or layer == &#39;last_hidden_state&#39;:
+                emb = output[&#39;last_hidden_state&#39;].cpu().detach().numpy()
+                emb = emb.mean(axis=1)
+
+            embs.append(emb)
+
+        embs = np.concatenate(embs)
+
     elif &#39;gpt&#39; in checkpoint.lower():
         tokens = preprocess_gpt_token_batch(seqs, tokenizer_embeddings)
-        tokens = tokens.to(model.device)
-        output = model(**tokens)
-
-        # tuple of (layer x (batch_size, seq_len, hidden_size))
-        h = output[&#39;hidden_states&#39;]
-        # (batch_size, seq_len, hidden_size)
-        embs = h[0].cpu().detach().numpy()
-        embs = embs.mean(axis=1)  # (batch_size, hidden_size)
+
+        embs = []
+
+        ds = Dataset.from_dict(tokens).with_format(&#34;torch&#34;)
+
+        for batch in DataLoader(ds, batch_size=batch_size, shuffle=False):
+            batch = {k: v.to(model.device) for k, v in batch.items()}
+
+            with torch.no_grad():
+                output = model(**batch)
+            torch.cuda.empty_cache()
+
+            # tuple of (layer x (batch_size, seq_len, hidden_size))
+            h = output[&#39;hidden_states&#39;]
+            # (batch_size, seq_len, hidden_size)
+            emb = h[0].cpu().detach().numpy()
+            emb = emb.mean(axis=1)  # (batch_size, hidden_size)
+
+            embs.append(emb)
+
+        embs = np.concatenate(embs)
+
     elif checkpoint.startswith(&#39;hkunlp/instructor&#39;):
         if instructor_prompt is None:
             instructor_prompt = &#34;Represent the short phrase for sentiment classification: &#34;
-        embs = model.encode([[instructor_prompt, x_i] for x_i in seqs], batch_size=32)
+        embs = model.encode([[instructor_prompt, x_i]
+                            for x_i in seqs], batch_size=batch_size)
 
     # sum over the embeddings
     embs = embs.sum(axis=0).reshape(1, -1)

docs/auggam/embed.html

@@ -83,17 +83,12 @@
 <td>Black-box model</td>
 <td>Finetune a single linear layer<br/>on top of LLM embeddings</td>
 </tr>
-<tr>
-<td style="text-align: left;">(Coming soon!)</td>
-<td>⌛</td>
-<td></td>
-<td>We plan to support other interpretable models like <a href="https://arxiv.org/abs/2205.12548">RLPrompt</a>, <a href="https://arxiv.org/abs/2007.04612">CBMs</a>, <a href="https://proceedings.neurips.cc/paper/2021/hash/251bd0442dfcc53b5a761e050f8022b8-Abstract.html">NAMs</a>, and <a href="https://arxiv.org/abs/2004.00221">NBDT</a></td>
-</tr>
 </tbody>
 </table>
 <p align="center">
-Demo notebooks <a href="https://github.com/csinva/imodelsX/tree/master/demo_notebooks">📖</a>, Doc <a href="https://csinva.io/imodelsX/">🗂️</a>, Reference code implementation 🔗, Research paper 📄
+<a href="https://github.com/csinva/imodelsX/tree/master/demo_notebooks">📖</a>Demo notebooks &emsp; <a href="https://csinva.io/imodelsX/">🗂️</a> Doc &emsp; 🔗 Reference code &emsp; 📄 Research paper
 </br>
+⌛ We plan to support other interpretable algorithms like <a href="https://arxiv.org/abs/2205.12548">RLPrompt</a>, <a href="https://arxiv.org/abs/2007.04612">CBMs</a>, and <a href="https://arxiv.org/abs/2004.00221">NBDT</a>. If you want to contribute an algorithm, feel free to open a PR 😄
 </p>
 <h1 id="quickstart">Quickstart</h1>
 <p><strong>Installation</strong>: <code>pip install <a title="imodelsx" href="#imodelsx">imodelsx</a></code> (or, for more control, clone and install from source)</p>

docs/index.html

@@ -32,6 +32,7 @@ <h1 class="title">Module <code>imodelsx.util</code></h1>
 from transformers import pipeline
 import datasets
 import numpy as np
+from collections import Counter
 
 
 def generate_ngrams_list(
@@ -42,6 +43,7 @@ <h1 class="title">Module <code>imodelsx.util</code></h1>
     parsing: str = &#39;&#39;,
     nlp_chunks=None,
     pad_starting_ngrams=False,
+    min_frequency=1,
 ):
     &#34;&#34;&#34;Get list of ngrams from sentence using a tokenizer
 
@@ -55,6 +57,8 @@ <h1 class="title">Module <code>imodelsx.util</code></h1>
         if all_ngrams=False, then pad starting ngrams with shorter length ngrams
         so that length of ngrams_list is the same as the initial sequence
         e.g. for ngrams=3 [&#34;the&#34;, &#34;the quick&#34;, &#34;the quick brown&#34;, &#34;quick brown fox&#34;, &#34;brown fox jumps&#34;, ...]
+    min_frequency: int
+        minimum frequency to be considered for the ngrams_list
     &#34;&#34;&#34;
 
     seqs = []
@@ -96,6 +100,10 @@ <h1 class="title">Module <code>imodelsx.util</code></h1>
         assert all_ngrams is False, &#34;pad_starting_ngrams only works when all_ngrams=False&#34;
         seqs_init = [&#39; &#39;.join(unigrams_list[:ngram_length]) for ngram_length in range(1, ngrams)]
         seqs = seqs_init + seqs
+    
+    freqs = Counter(seqs)
+
+    seqs = [seq for seq, freq in freqs.items() if freq &gt;= min_frequency]
 
     return seqs
 
@@ -177,7 +185,7 @@ <h1 class="title">Module <code>imodelsx.util</code></h1>
 <h2 class="section-title" id="header-functions">Functions</h2>
 <dl>
 <dt id="imodelsx.util.generate_ngrams_list"><code class="name flex">
-<span>def <span class="ident">generate_ngrams_list</span></span>(<span>sentence: str, ngrams: int, tokenizer_ngrams=None, all_ngrams=False, parsing: str = '', nlp_chunks=None, pad_starting_ngrams=False)</span>
+<span>def <span class="ident">generate_ngrams_list</span></span>(<span>sentence: str, ngrams: int, tokenizer_ngrams=None, all_ngrams=False, parsing: str = '', nlp_chunks=None, pad_starting_ngrams=False, min_frequency=1)</span>
 </code></dt>
 <dd>
 <div class="desc"><p>Get list of ngrams from sentence using a tokenizer</p>
@@ -189,7 +197,9 @@ <h2 id="params">Params</h2>
 pad_starting_ngrams: bool
 if all_ngrams=False, then pad starting ngrams with shorter length ngrams
 so that length of ngrams_list is the same as the initial sequence
-e.g. for ngrams=3 ["the", "the quick", "the quick brown", "quick brown fox", "brown fox jumps", &hellip;]</p></div>
+e.g. for ngrams=3 ["the", "the quick", "the quick brown", "quick brown fox", "brown fox jumps", &hellip;]
+min_frequency: int
+minimum frequency to be considered for the ngrams_list</p></div>
 <details class="source">
 <summary>
 <span>Expand source code</span>
@@ -202,6 +212,7 @@ <h2 id="params">Params</h2>
     parsing: str = &#39;&#39;,
     nlp_chunks=None,
     pad_starting_ngrams=False,
+    min_frequency=1,
 ):
     &#34;&#34;&#34;Get list of ngrams from sentence using a tokenizer
 
@@ -215,6 +226,8 @@ <h2 id="params">Params</h2>
         if all_ngrams=False, then pad starting ngrams with shorter length ngrams
         so that length of ngrams_list is the same as the initial sequence
         e.g. for ngrams=3 [&#34;the&#34;, &#34;the quick&#34;, &#34;the quick brown&#34;, &#34;quick brown fox&#34;, &#34;brown fox jumps&#34;, ...]
+    min_frequency: int
+        minimum frequency to be considered for the ngrams_list
     &#34;&#34;&#34;
 
     seqs = []
@@ -256,6 +269,10 @@ <h2 id="params">Params</h2>
         assert all_ngrams is False, &#34;pad_starting_ngrams only works when all_ngrams=False&#34;
         seqs_init = [&#39; &#39;.join(unigrams_list[:ngram_length]) for ngram_length in range(1, ngrams)]
         seqs = seqs_init + seqs
+    
+    freqs = Counter(seqs)
+
+    seqs = [seq for seq, freq in freqs.items() if freq &gt;= min_frequency]
 
     return seqs</code></pre>
 </details>

docs/util.html

@@ -36,6 +36,7 @@ def __init__(
         layer: str = 'last_hidden_state',
         ngrams: int = 2,
         all_ngrams: bool = False,
+        min_frequency: int = 1,
         tokenizer_ngrams=None,
         random_state=None,
         normalize_embs=False,
@@ -54,6 +55,8 @@ def __init__(
             Order of ngrams to extract. 1 for unigrams, 2 for bigrams, etc.
         all_ngrams
             Whether to use all order ngrams <= ngrams argument
+        min_frequency
+            minimum frequency of ngrams to be kept in the ngrams list.
         tokenizer_ngrams
             if None, defaults to spacy English tokenizer
         random_state
@@ -76,6 +79,7 @@ def __init__(
         self.layer = layer
         self.random_state = random_state
         self.all_ngrams = all_ngrams
+        self.min_frequency = min_frequency
         self.normalize_embs = normalize_embs
         self.fit_with_ngram_decomposition = fit_with_ngram_decomposition
         self.instructor_prompt = instructor_prompt
@@ -284,6 +288,7 @@ def _get_ngrams_list(self, X):
                 ngrams=self.ngrams,
                 tokenizer_ngrams=self.tokenizer_ngrams,
                 all_ngrams=self.all_ngrams,
+                min_frequency=self.min_frequency
             )
             all_ngrams |= set(seqs)
         return sorted(list(all_ngrams))

imodelsx/auggam/auggam.py

@@ -6,6 +6,7 @@
 from transformers import pipeline
 import datasets
 import numpy as np
+from collections import Counter
 
 
 def generate_ngrams_list(
@@ -16,6 +17,7 @@ def generate_ngrams_list(
     parsing: str = '',
     nlp_chunks=None,
     pad_starting_ngrams=False,
+    min_frequency=1,
 ):
     """Get list of ngrams from sentence using a tokenizer
 
@@ -29,6 +31,8 @@ def generate_ngrams_list(
         if all_ngrams=False, then pad starting ngrams with shorter length ngrams
         so that length of ngrams_list is the same as the initial sequence
         e.g. for ngrams=3 ["the", "the quick", "the quick brown", "quick brown fox", "brown fox jumps", ...]
+    min_frequency: int
+        minimum frequency to be considered for the ngrams_list
     """
 
     seqs = []
@@ -70,6 +74,10 @@ def generate_ngrams_list(
         assert all_ngrams is False, "pad_starting_ngrams only works when all_ngrams=False"
         seqs_init = [' '.join(unigrams_list[:ngram_length]) for ngram_length in range(1, ngrams)]
         seqs = seqs_init + seqs
+    
+    freqs = Counter(seqs)
+
+    seqs = [seq for seq, freq in freqs.items() if freq >= min_frequency]
 
     return seqs
 

imodelsx/util.py

@@ -24,7 +24,7 @@
 
 setuptools.setup(
     name="imodelsx",
-    version="0.20",
+    version="0.21",
     author="Chandan Singh, John X. Morris, Armin Askari",
     author_email="chansingh@microsoft.com",
     description="Library to explain a dataset in natural language.",

setup.py

@@ -15,6 +15,7 @@
         checkpoint='textattack/distilbert-base-uncased-rotten-tomatoes',
         ngrams=2,
         all_ngrams=True,  # also use lower-order ngrams
+        min_frequency=1
     )
     m.fit(dset['text'], dset['label'], batch_size=8)