benwtrent commented on issue #12342:
URL: https://github.com/apache/lucene/issues/12342#issuecomment-1656112434
OK ran on two more datasets, I only ran over the first 100k documents in my
data sets.
I think this exhausts our testing for Cohere, we need to find additional
data if this is not considered enough.
# EN
Reversed | Ordered | Random
:-------------------------:|:-------------------------:|:-------------------------:
|
|
# JA
Reversed | Ordered | Random
:-------------------------:|:-------------------------:|:-------------------------:
|
|
<details>
<summary> updating data creation (after download) </summary>
```python
import numpy as np
import pyarrow.parquet as pq
DATA_SETS =[
{"name": "wiki768", "files": [
"train-00000-of-00004-1a1932c9ca1c7152.parquet",
"train-00001-of-00004-f4a4f5540ade14b4.parquet",
"train-00002-of-00004-ff770df3ab420d14.parquet",
"train-00003-of-00004-85b3dbbc960e92ec.parquet",
]},{
"name": "wiki768en", "files": [
"0-en.parquet",
"1-en.parquet",
"2-en.parquet",
"3-en.parquet",
]},
{"name": "wiki768ja", "files": [
"0-ja.parquet",
"1-ja.parquet",
"2-ja.parquet",
"3-ja.parquet",
]}
]
def transform_queries(Q):
n, _ = Q.shape
return np.concatenate([Q, np.zeros((n, 1))], axis=-1, dtype=np.float32)
def transform_docs(D, norms):
n, d = D.shape
max_norm = magnitudes.max()
flipped_norms = np.copy(norms).reshape(n, 1)
transformed_data = np.concatenate([D, np.sqrt(max_norm**2 -
flipped_norms**2)], axis=-1, dtype=np.float32)
return transformed_data
def validate_array_match_upto_dim(arr1, arr2, dim_eq_upto):
assert np.allclose(arr1[:dim_eq_upto], arr2[:dim_eq_upto]), "data sets
are different"
def validate_dataset_match_upto_dim(arr1, arr2, dim_eq_upto):
n1, d1 = arr1.shape
n2, d2 = arr2.shape
assert n1 == n2, f"Shape does not map [{arr1.shape}] vs [{arr2.shape}]"
for i in range(n1):
validate_array_match_upto_dim(arr1[i], arr2[i], dim_eq_upto)
for ds in DATA_SETS:
name = ds["name"]
tb1 = pq.read_table(ds["files"][0], columns=['emb'])
tb2 = pq.read_table(ds["files"][1], columns=['emb'])
tb3 = pq.read_table(ds["files"][2], columns=['emb'])
tb4 = pq.read_table(ds["files"][3], columns=['emb'])
np1 = tb1[0].to_numpy()
np2 = tb2[0].to_numpy()
np3 = tb3[0].to_numpy()
np4 = tb4[0].to_numpy()
np_total = np.concatenate((np1, np2, np3, np4))
#Have to convert to a list here to get
#the numpy ndarray's shape correct later
#There's probably a better way...
flat_ds = list()
for vec in np_total:
flat_ds.append(vec)
np_flat_ds = np.array(flat_ds)
row_count = np_flat_ds.shape[0]
query_count = 10_000
training_rows = row_count - query_count
print(f"{name} num rows: {training_rows}")
transformed_queries = transform_queries(np_flat_ds[training_rows:-1])
validate_dataset_match_upto_dim(transformed_queries,
np_flat_ds[training_rows:-1], 768)
with open(f"{name}-transform.test", "w") as out_f:
transformed_queries.tofile(out_f)
with open(f"{name}.test", "w") as out_f:
np_flat_ds[training_rows:-1].tofile(out_f)
transformed_queries.tofile(out_f)
magnitudes = np.linalg.norm(np_flat_ds[0:training_rows], axis=1)
indices = np.argsort(magnitudes)
transformed_np_flat_ds = transform_docs(np_flat_ds[0:training_rows],
magnitudes)
validate_dataset_match_upto_dim(transformed_np_flat_ds,
np_flat_ds[0:training_rows], 768)
transformed_np_flat_ds_sorted = transformed_np_flat_ds[indices]
np_flat_ds_sorted = np_flat_ds[indices]
with open(f"{name}.random-transform.train", "w") as out_f:
transformed_np_flat_ds.tofile(out_f)
with open(f"{name}.ordered-transform.train", "w") as out_f:
transformed_np_flat_ds_sorted.tofile(out_f)
with open(f"{name}.reversed-transform.train", "w") as out_f:
np.flip(transformed_np_flat_ds_sorted, axis=0).tofile(out_f)
with open(f"{name}.random.train", "w") as out_f:
np.flip(np_flat_ds[0:training_rows], axis=0).tofile(out_f)
with open(f"{name}.reversed.train", "w") as out_f:
np.flip(np_flat_ds_sorted, axis=0).tofile(out_f)
with open(f"{name}.ordered.train", "w") as out_f:
np_flat_ds_sorted.tofile(out_f)
```
</details>
<details>
<summary>Useful parsing & plotting functions</summary>
```python
def parse_console_output(terminal_output):
# Regular expression patterns to extract recall and latency values
recall_pattern = r"(?:\n\d+\.\d+)"
latency_pattern = r"([\t, ]\d+\.\d+\t\d)"
recall_values = [float(match.strip()) for match in
re.findall(recall_pattern, terminal_output)]
latency_values = [float(match.split()[0]) for match in
re.findall(latency_pattern, terminal_output)]
return (recall_values, latency_values)
def plot_things(name, baseline_recall, baseline_latency, transformed_recall,
transform_latency):
# Plotting series one: transformed_recall vs transform_latency
plt.plot(transform_latency, transformed_recall, marker='o',
label='transformed')
# Plotting series two: baseline_recall vs baseline_latency
plt.plot(baseline_latency, baseline_recall, marker='o', label='original
(baseline)')
# Add labels and title
plt.xlabel('Latency')
plt.ylabel('Recall')
plt.title(f"{name} Transformed vs Baseline recall & latency")
plt.legend()
# Show the plot
plt.grid(True)
plt.show()
```
To use them:
```python
transformed_terminal_output = """
WARNING: Gnuplot module not present; will not make charts
recall latency nDoc fanout maxConn beamWidth visited index ms
WARNING: Using incubator modules: jdk.incubator.vector
Jul 28, 2023 12:01:58 PM
org.apache.lucene.internal.vectorization.PanamaVectorizationProvider <init>
INFO: Java vector incubator API enabled; uses preferredBitSize=128
0.863 0.32 100000 0 48 200 10 0 1.00
post-filter
...
"""
baseline_terminal_output = """
WARNING: Gnuplot module not present; will not make charts
recall latency nDoc fanout maxConn beamWidth visited index ms
WARNING: Using incubator modules: jdk.incubator.vector
Jul 28, 2023 12:34:59 PM
org.apache.lucene.internal.vectorization.PanamaVectorizationProvider <init>
INFO: Java vector incubator API enabled; uses preferredBitSize=128
0.816 0.23 100000 0 48 200 10 0 1.00
post-filter
...
"""
name = "WikiEN-Reversed"
transformed_recall, transform_latency =
parse_console_output(transformed_terminal_output)
baseline_recall, baseline_latency =
parse_console_output(baseline_terminal_output)
plot_things(name, baseline_recall, baseline_latency, transformed_recall,
transform_latency)
```
</details>
<details>
<summary>data downloading script</summary>
```sh
#!/bin/sh
# Japanese
curl -L
https://huggingface.co/api/datasets/Cohere/wikipedia-22-12-ja-embeddings/parquet/Cohere--wikipedia-22-12-ja-embeddings/train/0.parquet
-o 0-ja.parquet
curl -L
https://huggingface.co/api/datasets/Cohere/wikipedia-22-12-ja-embeddings/parquet/Cohere--wikipedia-22-12-ja-embeddings/train/1.parquet
-o 1-ja.parquet
curl -L
https://huggingface.co/api/datasets/Cohere/wikipedia-22-12-ja-embeddings/parquet/Cohere--wikipedia-22-12-ja-embeddings/train/33.parquet
-o 2-ja.parquet
curl -L
https://huggingface.co/api/datasets/Cohere/wikipedia-22-12-ja-embeddings/parquet/Cohere--wikipedia-22-12-ja-embeddings/train/34.parquet
-o 3-ja.parquet
# English
curl -L
https://huggingface.co/api/datasets/Cohere/wikipedia-22-12-en-embeddings/parquet/Cohere--wikipedia-22-12-en-embeddings/train/0.parquet
-o 0-en.parquet
curl -L
https://huggingface.co/api/datasets/Cohere/wikipedia-22-12-en-embeddings/parquet/Cohere--wikipedia-22-12-en-embeddings/train/1.parquet
-o 1-en.parquet
curl -L
https://huggingface.co/api/datasets/Cohere/wikipedia-22-12-en-embeddings/parquet/Cohere--wikipedia-22-12-en-embeddings/train/251.parquet
-o 2-en.parquet
curl -L
https://huggingface.co/api/datasets/Cohere/wikipedia-22-12-en-embeddings/parquet/Cohere--wikipedia-22-12-en-embeddings/train/252.parquet
-o 3-en.parquet
```
</details>
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