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Ringkasan
Tutorial ini menunjukkan cara menggunakan embedding dari Gemini API untuk mendeteksi potensi pencilan dalam set data Anda. Anda akan memvisualisasikan satu set dari 20 set data Newsgroup menggunakan t-SNE dan mendeteksi pencilan di luar radius tertentu dari titik pusat setiap cluster kategori.
Untuk mengetahui informasi lebih lanjut terkait cara memulai penyematan yang dihasilkan dari Gemini API, lihat panduan memulai Python.
Prasyarat
Anda dapat menjalankan panduan memulai ini di Google Colab.
Untuk menyelesaikan panduan memulai ini di lingkungan pengembangan Anda sendiri, pastikan lingkungan Anda memenuhi persyaratan berikut:
- Python 3.9 dan yang lebih baru
- Penginstalan
jupyter
untuk menjalankan notebook.
Penyiapan
Pertama, download dan instal library Python Gemini API.
pip install -U -q google.generativeai
import re
import tqdm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import google.generativeai as genai
# Used to securely store your API key
from google.colab import userdata
from sklearn.datasets import fetch_20newsgroups
from sklearn.manifold import TSNE
Mengambil Kunci API
Sebelum dapat menggunakan Gemini API, Anda harus mendapatkan kunci API terlebih dahulu. Jika Anda belum memilikinya, buat kunci dengan sekali klik di Google AI Studio.
Di Colab, tambahkan kunci ke pengelola rahasia di bagian "Tarik" di panel kiri. Beri nama API_KEY
.
Setelah Anda memiliki kunci API, teruskan ke SDK. Anda dapat melakukannya dengan dua cara:
- Masukkan kunci di variabel lingkungan
GOOGLE_API_KEY
(SDK akan otomatis mengambilnya dari sana). - Teruskan kunci ke
genai.configure(api_key=...)
genai.configure(api_key=GOOGLE_API_KEY)
for m in genai.list_models():
if 'embedContent' in m.supported_generation_methods:
print(m.name)
models/embedding-001 models/embedding-001
Menyiapkan set data
20 Newsgroups Text Dataset berisi 18.000 postingan grup berita tentang 20 topik yang dibagi menjadi set pelatihan dan pengujian. Pembagian antara set data pelatihan dan pengujian didasarkan pada pesan yang diposting sebelum dan setelah tanggal tertentu. Tutorial ini menggunakan subset pelatihan.
newsgroups_train = fetch_20newsgroups(subset='train')
# View list of class names for dataset
newsgroups_train.target_names
['alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x', 'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc']
Berikut adalah contoh pertama dalam set pelatihan.
idx = newsgroups_train.data[0].index('Lines')
print(newsgroups_train.data[0][idx:])
Lines: 15 I was wondering if anyone out there could enlighten me on this car I saw the other day. It was a 2-door sports car, looked to be from the late 60s/ early 70s. It was called a Bricklin. The doors were really small. In addition, the front bumper was separate from the rest of the body. This is all I know. If anyone can tellme a model name, engine specs, years of production, where this car is made, history, or whatever info you have on this funky looking car, please e-mail. Thanks, - IL ---- brought to you by your neighborhood Lerxst ----
# Apply functions to remove names, emails, and extraneous words from data points in newsgroups.data
newsgroups_train.data = [re.sub(r'[\w\.-]+@[\w\.-]+', '', d) for d in newsgroups_train.data] # Remove email
newsgroups_train.data = [re.sub(r"\([^()]*\)", "", d) for d in newsgroups_train.data] # Remove names
newsgroups_train.data = [d.replace("From: ", "") for d in newsgroups_train.data] # Remove "From: "
newsgroups_train.data = [d.replace("\nSubject: ", "") for d in newsgroups_train.data] # Remove "\nSubject: "
# Cut off each text entry after 5,000 characters
newsgroups_train.data = [d[0:5000] if len(d) > 5000 else d for d in newsgroups_train.data]
# Put training points into a dataframe
df_train = pd.DataFrame(newsgroups_train.data, columns=['Text'])
df_train['Label'] = newsgroups_train.target
# Match label to target name index
df_train['Class Name'] = df_train['Label'].map(newsgroups_train.target_names.__getitem__)
df_train
Selanjutnya, ambil sampel dari beberapa data dengan mengambil 150 titik data dalam {i>dataset<i} pelatihan dan memilih beberapa kategori. Tutorial ini menggunakan kategori sains.
# Take a sample of each label category from df_train
SAMPLE_SIZE = 150
df_train = (df_train.groupby('Label', as_index = False)
.apply(lambda x: x.sample(SAMPLE_SIZE))
.reset_index(drop=True))
# Choose categories about science
df_train = df_train[df_train['Class Name'].str.contains('sci')]
# Reset the index
df_train = df_train.reset_index()
df_train
df_train['Class Name'].value_counts()
sci.crypt 150 sci.electronics 150 sci.med 150 sci.space 150 Name: Class Name, dtype: int64
Membuat embedding
Di bagian ini, Anda akan melihat cara menghasilkan embeddings untuk berbagai teks dalam dataframe menggunakan embeddings dari Gemini API.
Perubahan API pada Embeddings dengan model embedding-001
Untuk model embedding baru, embedding-001, ada parameter jenis tugas baru dan judul opsional (hanya valid dengan task_type=RETRIEVAL_DOCUMENT
).
Parameter baru ini hanya berlaku untuk model embedding terbaru.Jenis tugasnya adalah:
Jenis Tugas | Deskripsi |
---|---|
RETRIEVAL_QUERY | Menentukan bahwa teks yang diberikan merupakan kueri dalam setelan penelusuran/pengambilan. |
RETRIEVAL_DOCUMENT | Menentukan bahwa teks yang diberikan adalah dokumen dalam setelan penelusuran/pengambilan. |
SEMANTIC_SIMILARITY | Menentukan bahwa teks yang diberikan akan digunakan untuk Kemiripan Teks Semantik (STS). |
KLASIFIKASI | Menentukan bahwa embedding akan digunakan untuk klasifikasi. |
PENGELOLAAN | Menentukan bahwa embedding akan digunakan untuk pengelompokan. |
from tqdm.auto import tqdm
tqdm.pandas()
from google.api_core import retry
def make_embed_text_fn(model):
@retry.Retry(timeout=300.0)
def embed_fn(text: str) -> list[float]:
# Set the task_type to CLUSTERING.
embedding = genai.embed_content(model=model,
content=text,
task_type="clustering")['embedding']
return np.array(embedding)
return embed_fn
def create_embeddings(df):
model = 'models/embedding-001'
df['Embeddings'] = df['Text'].progress_apply(make_embed_text_fn(model))
return df
df_train = create_embeddings(df_train)
df_train.drop('index', axis=1, inplace=True)
0%| | 0/600 [00:00<?, ?it/s]
Pengurangan dimensi
Dimensi vektor embedding dokumen adalah 768. Untuk memvisualisasikan cara dokumen yang disematkan dikelompokkan bersama, Anda perlu menerapkan pengurangan dimensi karena Anda hanya dapat memvisualisasikan embedding dalam ruang 2D atau 3D. Dokumen yang mirip secara kontekstual harus memiliki jarak yang berdekatan dalam ruang dibandingkan dengan dokumen yang tidak serupa.
len(df_train['Embeddings'][0])
768
# Convert df_train['Embeddings'] Pandas series to a np.array of float32
X = np.array(df_train['Embeddings'].to_list(), dtype=np.float32)
X.shape
(600, 768)
Anda akan menerapkan pendekatan t-Distributed Stochastic Neighbor Embedding (t-SNE) untuk melakukan pengurangan dimensi. Teknik ini mengurangi jumlah dimensi, sekaligus mempertahankan cluster (titik yang berdekatan tetap berdekatan). Untuk data asli, model mencoba membuat distribusi yang titik data lainnya adalah "tetangga" (misalnya, keduanya memiliki arti yang serupa). Langkah ini kemudian mengoptimalkan fungsi objektif untuk menjaga distribusi yang serupa dalam visualisasi.
tsne = TSNE(random_state=0, n_iter=1000)
tsne_results = tsne.fit_transform(X)
df_tsne = pd.DataFrame(tsne_results, columns=['TSNE1', 'TSNE2'])
df_tsne['Class Name'] = df_train['Class Name'] # Add labels column from df_train to df_tsne
df_tsne
fig, ax = plt.subplots(figsize=(8,6)) # Set figsize
sns.set_style('darkgrid', {"grid.color": ".6", "grid.linestyle": ":"})
sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='Set2')
sns.move_legend(ax, "upper left", bbox_to_anchor=(1, 1))
plt.title('Scatter plot of news using t-SNE')
plt.xlabel('TSNE1')
plt.ylabel('TSNE2');
Deteksi outlier
Untuk menentukan titik mana yang tidak wajar, Anda akan menentukan titik mana yang merupakan inlier dan {i>outlier<i}. Mulailah dengan menemukan sentroid, atau lokasi yang mewakili pusat gugus, dan gunakan jarak untuk menentukan titik-titik yang merupakan pencilan.
Mulai dengan mendapatkan sentroid dari setiap kategori.
def get_centroids(df_tsne):
# Get the centroid of each cluster
centroids = df_tsne.groupby('Class Name').mean()
return centroids
centroids = get_centroids(df_tsne)
centroids
def get_embedding_centroids(df):
emb_centroids = dict()
grouped = df.groupby('Class Name')
for c in grouped.groups:
sub_df = grouped.get_group(c)
# Get the centroid value of dimension 768
emb_centroids[c] = np.mean(sub_df['Embeddings'], axis=0)
return emb_centroids
emb_c = get_embedding_centroids(df_train)
Tempatkan setiap sentroid yang Anda temukan terhadap titik lainnya.
# Plot the centroids against the cluster
fig, ax = plt.subplots(figsize=(8,6)) # Set figsize
sns.set_style('darkgrid', {"grid.color": ".6", "grid.linestyle": ":"})
sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='Set2');
sns.scatterplot(data=centroids, x='TSNE1', y='TSNE2', color="black", marker='X', s=100, label='Centroids')
sns.move_legend(ax, "upper left", bbox_to_anchor=(1, 1))
plt.title('Scatter plot of news using t-SNE with centroids')
plt.xlabel('TSNE1')
plt.ylabel('TSNE2');
Pilih radius. Apa pun yang berada di luar batasan ini dari sentroid kategori tersebut dianggap sebagai pencilan.
def calculate_euclidean_distance(p1, p2):
return np.sqrt(np.sum(np.square(p1 - p2)))
def detect_outlier(df, emb_centroids, radius):
for idx, row in df.iterrows():
class_name = row['Class Name'] # Get class name of row
# Compare centroid distances
dist = calculate_euclidean_distance(row['Embeddings'],
emb_centroids[class_name])
df.at[idx, 'Outlier'] = dist > radius
return len(df[df['Outlier'] == True])
range_ = np.arange(0.3, 0.75, 0.02).round(decimals=2).tolist()
num_outliers = []
for i in range_:
num_outliers.append(detect_outlier(df_train, emb_c, i))
# Plot range_ and num_outliers
fig = plt.figure(figsize = (14, 8))
plt.rcParams.update({'font.size': 12})
plt.bar(list(map(str, range_)), num_outliers)
plt.title("Number of outliers vs. distance of points from centroid")
plt.xlabel("Distance")
plt.ylabel("Number of outliers")
for i in range(len(range_)):
plt.text(i, num_outliers[i], num_outliers[i], ha = 'center')
plt.show()
Anda dapat memilih radius yang ingin digunakan, bergantung pada sensitivitas pendeteksi anomali. Untuk saat ini, 0,62 digunakan, tetapi Anda dapat mengubah nilai ini.
# View the points that are outliers
RADIUS = 0.62
detect_outlier(df_train, emb_c, RADIUS)
df_outliers = df_train[df_train['Outlier'] == True]
df_outliers.head()
# Use the index to map the outlier points back to the projected TSNE points
outliers_projected = df_tsne.loc[df_outliers['Outlier'].index]
Tempatkan {i>outlier <i}dan tunjukkan menggunakan warna merah transparan.
fig, ax = plt.subplots(figsize=(8,6)) # Set figsize
plt.rcParams.update({'font.size': 10})
sns.set_style('darkgrid', {"grid.color": ".6", "grid.linestyle": ":"})
sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', hue='Class Name', palette='Set2');
sns.scatterplot(data=centroids, x='TSNE1', y='TSNE2', color="black", marker='X', s=100, label='Centroids')
# Draw a red circle around the outliers
sns.scatterplot(data=outliers_projected, x='TSNE1', y='TSNE2', color='red', marker='o', alpha=0.5, s=90, label='Outliers')
sns.move_legend(ax, "upper left", bbox_to_anchor=(1, 1))
plt.title('Scatter plot of news with outliers projected with t-SNE')
plt.xlabel('TSNE1')
plt.ylabel('TSNE2');
Gunakan nilai indeks datafame untuk mencetak beberapa contoh {i>outlier <i}di setiap kategori. Di sini, titik data pertama dari setiap kategori dicetak. Jelajahi titik lain di setiap kategori untuk melihat data yang dianggap sebagai pencilan, atau anomali.
sci_crypt_outliers = df_outliers[df_outliers['Class Name'] == 'sci.crypt']
print(sci_crypt_outliers['Text'].iloc[0])
Re: Source of random bits on a Unix workstation Lines: 44 Nntp-Posting-Host: sandstorm >>For your application, what you can do is to encrypt the real-time clock >>value with a secret key. Well, almost.... If I only had to solve the problem for myself, and were willing to have to type in a second password whenever I logged in, it could work. However, I'm trying to create a solution that anyone can use, and which, once installed, is just as effortless to start up as the non-solution of just using xhost to control access. I've got religeous problems with storing secret keys on multiuser computers. >For a good discussion of cryptographically "good" random number >generators, check out the draft-ietf-security-randomness-00.txt >Internet Draft, available at your local friendly internet drafts >repository. Thanks for the pointer! It was good reading, and I liked the idea of using several unrelated sources with a strong mixing function. However, unless I missed something, the only source they suggested that seems available, and unguessable by an intruder, when a Unix is fresh-booted, is I/O buffers related to network traffic. I believe my solution basically uses that strategy, without requiring me to reach into the kernel. >A reasonably source of randomness is the output of a cryptographic >hash function , when fed with a large amount of >more-or-less random data. For example, running MD5 on /dev/mem is a >slow, but random enough, source of random bits; there are bound to be >128 bits of entropy in the tens of megabytes of data in >a modern workstation's memory, as a fair amount of them are system >timers, i/o buffers, etc. I heard about this solution, and it sounded good. Then I heard that folks were experiencing times of 30-60 seconds to run this, on reasonably-configured workstations. I'm not willing to add that much delay to someone's login process. My approach takes a second or two to run. I'm considering writing the be-all and end-all of solutions, that launches the MD5, and simultaneously tries to suck bits off the net, and if the net should be sitting __SO__ idle that it can't get 10K after compression before MD5 finishes, use the MD5. This way I could have guaranteed good bits, and a deterministic upper bound on login time, and still have the common case of login take only a couple of extra seconds. -Bennett
sci_elec_outliers = df_outliers[df_outliers['Class Name'] == 'sci.electronics']
print(sci_elec_outliers['Text'].iloc[0])
Re: Laser vs Bubblejet? Reply-To: Distribution: world X-Mailer: cppnews \\(Revision: 1.20 \\) Organization: null Lines: 53 Here is a different viewpoint. > FYI: The actual horizontal dot placement resoution of an HP > deskjet is 1/600th inch. The electronics and dynamics of the ink > cartridge, however, limit you to generating dots at 300 per inch. > On almost any paper, the ink wicks more than 1/300th inch anyway. > > The method of depositing and fusing toner of a laster printer > results in much less spread than ink drop technology. In practice there is little difference in quality but more care is needed with inkjet because smudges etc. can happen. > It doesn't take much investigation to see that the mechanical and > electronic complement of a laser printer is more complex than > inexpensive ink jet printers. Recall also that laser printers > offer a much higher throughput: 10 ppm for a laser versus about 1 > ppm for an ink jet printer. A cheap laser printer does not manage that sort of throughput and on top of that how long does the _first_ sheet take to print? Inkjets are faster than you say and in both cases the computer often has trouble keeping up with the printer. A sage said to me: "Do you want one copy or lots of copies?", "One", "Inkjet". > Something else to think about is the cost of consumables over the > life of the printer. A 3000 page yield toner cartridge is about > $US 75-80 at discount while HP high capacity > cartridges are about $US 22 at discount. It could be that over the > life cycle of the printer that consumables for laser printers are > less than ink jet printers. It is getting progressively closer > between the two technologies. Laser printers are usually desinged > for higher duty cycles in pages per month and longer product > replacement cycles. Paper cost is the same and both can use refills. Long term the laserprinter will need some expensive replacement parts and on top of that are the amortisation costs which favour the lowest purchase cost printer. HP inkjets understand PCL so in many cases a laserjet driver will work if the software package has no inkjet driver. There is one wild difference between the two printers: a laserprinter is a page printer whilst an inkjet is a line printer. This means that a laserprinter can rotate graphic images whilst an inkjet cannot. Few drivers actually use this facility. TC. E-mail: or
sci_med_outliers = df_outliers[df_outliers['Class Name'] == 'sci.med']
print(sci_med_outliers['Text'].iloc[0])
Re: THE BACK MACHINE - Update Organization: University of Nebraska--Lincoln Lines: 15 Distribution: na NNTP-Posting-Host: unlinfo.unl.edu I have a BACK MACHINE and have had one since January. While I have not found it to be a panacea for my back pain, I think it has helped somewhat. It MAINLY acts to stretch muscles in the back and prevent spasms associated with pain. I am taking less pain medication than I was previously. The folks at BACK TECHNOLOGIES are VERY reluctant to honor their return policy. They extended my "warranty" period rather than allow me to return the machine when, after the first month or so, I was not thrilled with it. They encouraged me to continue to use it, abeit less vigourously. Like I said, I can't say it is a cure-all, but it keeps me stretched out and I am in less pain. -- *********************************************************************** Dale M. Webb, DVM, PhD * 97% of the body is water. The Veterinary Diagnostic Center * other 3% keeps you from drowning. University of Nebraska, Lincoln *
sci_space_outliers = df_outliers[df_outliers['Class Name'] == 'sci.space']
print(sci_space_outliers['Text'].iloc[0])
MACH 25 landing site bases? Article-I.D.: aurora.1993Apr5.193829.1 Organization: University of Alaska Fairbanks Lines: 7 Nntp-Posting-Host: acad3.alaska.edu The supersonic booms hear a few months ago over I belive San Fran, heading east of what I heard, some new super speed Mach 25 aircraft?? What military based int he direction of flight are there that could handle a Mach 25aircraft on its landing decent?? Odd question?? == Michael Adams, -- I'm not high, just jacked
Langkah berikutnya
Sekarang Anda telah membuat detektor anomali menggunakan embeddings. Coba gunakan data tekstual Anda sendiri untuk memvisualisasikannya sebagai embedding, dan pilih beberapa data yang terikat sedemikian rupa sehingga Anda dapat mendeteksi pencilan. Anda dapat melakukan pengurangan dimensi untuk menyelesaikan langkah visualisasi. Perhatikan bahwa t-SNE baik dalam pengelompokan input, tetapi bisa membutuhkan waktu yang lebih lama untuk dikonvergensi atau mungkin terjebak pada nilai minimum lokal. Jika Anda mengalami masalah ini, teknik lain yang dapat Anda pertimbangkan adalah analisis komponen utama (PCA).
Untuk mempelajari lebih lanjut tentang bagaimana Anda dapat menggunakan embeddings, lihat tutorial lainnya: