lazuxd

import pandas as pd
import re
from os import system, listdir
from os.path import isfile, join
from random import shuffle

system('wget "http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"')
system('tar -xzf "aclImdb_v1.tar.gz"')

def create_data_frame(folder: str) -> pd.DataFrame:

lazuxd

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from joblib import dump, load # used for saving and loading sklearn objects
from scipy.sparse import save_npz, load_npz # used for saving and loading sparse matrices

system("mkdir 'data_preprocessors'")
system("mkdir 'vectorized_data'")


# Unigram Counts

lazuxd

from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import train_test_split
from scipy.sparse import csr_matrix
import numpy as np

def train_and_show_scores(X: csr_matrix, y: np.array, title: str) -> None:
    X_train, X_valid, y_train, y_valid = train_test_split(
        X, y, train_size=0.75, stratify=y
    )

lazuxd

from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import uniform

X_train = X_train_bigram_tf_idf


# Phase 1: loss, learning rate and initial learning rate

clf = SGDClassifier()

lazuxd

# Phase 2: penalty and alpha

clf = SGDClassifier()

distributions = dict(
    penalty=['l1', 'l2', 'elasticnet'],
    alpha=uniform(loc=1e-6, scale=1e-4)
)

random_search_cv = RandomizedSearchCV(

lazuxd

system("mkdir 'classifiers'")

sgd_classifier = random_search_cv.best_estimator_

dump(random_search_cv.best_estimator_, 'classifiers/sgd_classifier.joblib')

# sgd_classifier = load('classifiers/sgd_classifier.joblib')

lazuxd

X_test = bigram_vectorizer.transform(imdb_test['text'].values)
X_test = bigram_tf_idf_transformer.transform(X_test)
y_test = imdb_test['label'].values

score = sgd_classifier.score(X_test, y_test)
print(score)

lazuxd

import numpy as np

class MLClassifier:
    def fit(self, x: np.ndarray, y: np.ndarray) -> None:
        '''
        x - numpy array of shape (n, d); n = #observations; d = #variables
        y - numpy array of shape (n,)
        '''
        # no. of variables / dimension
        self.d = x.shape[1]

lazuxd

import pandas as pd
from sklearn.model_selection import train_test_split

df = pd.read_csv('heart.csv')

(x_train, x_test, y_train, y_test) = train_test_split(
    df.iloc[:, 0:13].values, df.iloc[:, 13].values, train_size=0.8)

mlc = MLClassifier()
mlc.fit(x_train, y_train)

lazuxd

from tensorflow import Tensor
from tensorflow.keras.layers import Input, Conv2D, ReLU, BatchNormalization,\
                                    Add, AveragePooling2D, Flatten, Dense
from tensorflow.keras.models import Model

def relu_bn(inputs: Tensor) -> Tensor:
    relu = ReLU()(inputs)
    bn = BatchNormalization()(relu)
    return bn