FeryET

برای شروع میتوانید یک دایرکتوری در سرور خارجی ایجاد کنید و وارد آن شوید.

mkdir vmess
cd vmess

FeryET

import os
import shutil
import gzip
from pmaw import PushshiftAPI
import datetime as dt
from tqdm import tqdm
import pandas as pd
from pathlib import Path

# Creating data folder

FeryET

              precision    recall  f1-score   support

      Normal       0.98      0.69      0.81       234
   Pneumonia       0.84      0.99      0.91       390

    accuracy                           0.88       624
   macro avg       0.91      0.84      0.86       624
weighted avg       0.89      0.88      0.87       624

test_loss: 0.4041681243823125

FeryET

==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
├─Sequential: 1-1                        [-1, 576, 8, 8]           --
|    └─ConvBNActivation: 2-1             [-1, 16, 128, 128]        (464)
|    └─InvertedResidual: 2-2             [-1, 16, 64, 64]          (744)
|    └─InvertedResidual: 2-3             [-1, 24, 32, 32]          (3,864)
|    └─InvertedResidual: 2-4             [-1, 24, 32, 32]          (5,416)
|    └─InvertedResidual: 2-5             [-1, 40, 16, 16]          (13,736)
|    └─InvertedResidual: 2-6             [-1, 40, 16, 16]          (57,264)

FeryET

def load_pretrained():
    pretrained_model = torchvision.models.mobilenetv3.mobilenet_v3_small(pretrained=True,
                                                                         progress=True)
    return pretrained_model.features


class PneumoniaNet(nn.Module):
    def __init__(self, 
                 input_dim,
                 finetune=False):

FeryET

folds = RepeatedStratifiedKFold(n_splits=10, n_repeats=10)
vectorizer = TomotopyLDAVectorizer(num_of_topics=15, workers=workers, min_df=min_df,
                                   rm_top=rm_top)
clf = SVC()
pca = PCA(n_components=0.95)
pipe = Pipeline([("vectorizer", vectorizer), ("scalar", StandardScaler()),
                 ("classifier", clf)])


results = cross_val_score(pipe, docs, y_true, cv=folds, n_jobs=2, verbose=1,

FeryET

def plot_topic_clusters(ax, x2d, y, labels):
    ax.set_aspect("equal")
    colors = cm.get_cmap("Spectral", len(labels))
    for i, l in enumerate(labels):
        c = colors(i / len(labels))
        ax.scatter(x2d[y == i, 0], x2d[y == i, 1], color=c, label=l, alpha=0.7)
    ax.grid()
    ax.legend()
    ax.set(adjustable='box', aspect='equal')
    return ax

FeryET

vectorizer = TomotopyLDAVectorizer(num_of_topics=num_of_topics,
                                   workers=workers, min_df=min_df,
                                   rm_top=rm_top)

x_train = vectorizer.fit_transform(docs_train)
x_test = vectorizer.transform(docs_test)

FeryET

hdp_model = HDPModel(min_df=min_df, rm_top=rm_top)
hdp_model.optim_interval = 5
for d in docs_train:
    hdp_model.add_doc(d)
hdp_model.burn_in = 100
hdp_model.train(0, workers=workers)
for i in range(0, 1000, 10):
    hdp_model.train(10, workers=workers)
    print('Iteration: {}\tLog-likelihood: {}\tNum. of topics: {}'.format(i, hdp_model.ll_per_word, hdp_model.live_k))

FeryET

processor = SpacyCleaner(chunksize=1000, workers=workers)
docs = processor.transform(raw_docs)