curiousily

app_infos = []

for ap in tqdm(app_packages):
  info = app(ap, lang='en', country='us')
  del info['comments']
  app_infos.append(info)

curiousily

app_packages = [
  'com.anydo',
  'com.todoist',
  'com.ticktick.task',
  'com.habitrpg.android.habitica',
  'cc.forestapp',
  'com.oristats.habitbull',
  'com.levor.liferpgtasks',
  'com.habitnow',
  'com.microsoft.todos',

curiousily

import json
import pandas as pd
from tqdm import tqdm

import seaborn as sns
import matplotlib.pyplot as plt

from pygments import highlight
from pygments.lexers import JsonLexer
from pygments.formatters import TerminalFormatter

curiousily

y_pred = model.predict(X_test)

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model.evaluate(X_test, y_test)

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history = model.fit(
    X_train, y_train,
    epochs=20,
    batch_size=32,
    validation_split=0.1,
    shuffle=False
)

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model = keras.Sequential()
model.add(
    keras.layers.Bidirectional(
      keras.layers.LSTM(
          units=128,
          input_shape=[X_train.shape[1], X_train.shape[2]]
      )
    )
)
model.add(keras.layers.Dropout(rate=0.5))

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enc = OneHotEncoder(handle_unknown='ignore', sparse=False)

enc = enc.fit(y_train)

y_train = enc.transform(y_train)
y_test = enc.transform(y_test)

curiousily

print(X_train.shape, y_train.shape)

curiousily

TIME_STEPS = 200
STEP = 40

X_train, y_train = create_dataset(
    df_train[['x_axis', 'y_axis', 'z_axis']],
    df_train.activity,
    TIME_STEPS,
    STEP
)