first_ml_test.py

# -*- coding: utf-8 -*-
"""first_ML_test.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1-rN9s_rinkl3iM8Bmlkm2eS6ZIOyHzOm
"""

from __future__ import absolute_import, division, print_function, unicode_literals

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import clear_output
from six.moves import urllib

from tensorflow.keras.layers import StringLookup, IntegerLookup, Normalization


import tensorflow as tf

dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv') # training data
dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv') # testing data
y_train = dftrain.pop('survived')
y_eval = dfeval.pop('survived')



CATEGORICAL_COLUMNS = ["sex", "class", "deck", "embark_town", "alone", "n_siblings_spouses", "parch"]
NUMERIC_COLUMNS = ["age", "fare"]

feature_preprocessors = {}

# Für kategoriale Daten
for feature_name in CATEGORICAL_COLUMNS:
    vocabulary = dftrain[feature_name].unique()
    if dftrain[feature_name].dtype == 'object':  # String-basierte Kategorien
        feature_preprocessors[feature_name] = StringLookup(vocabulary=vocabulary, output_mode='one_hot')
    else:  # Integer-basierte Kategorien
        feature_preprocessors[feature_name] = IntegerLookup(vocabulary=vocabulary, output_mode='one_hot')

for feature_name in NUMERIC_COLUMNS:
    # Daten vorbereiten
    dftrain[feature_name] = pd.to_numeric(dftrain[feature_name], errors='coerce')
    dftrain[feature_name] = dftrain[feature_name].fillna(0)  # Fehlende Werte auffüllen

    # Normalizer erstellen und anpassen
    normalizer = Normalization(axis=None)
    normalizer.adapt(dftrain[feature_name].to_numpy())  # Zu Numpy-Array konvertieren
    feature_preprocessors[feature_name] = normalizer

def make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):
  def input_function():  # inner function, this will be returned
    ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))  # create tf.data.Dataset object with data and its label
    if shuffle:
      ds = ds.shuffle(1000)  # randomize order of data
    ds = ds.batch(batch_size).repeat(num_epochs)  # split dataset into batches of 32 and repeat process for number of epochs
    return ds  # return a batch of the dataset
  return input_function  # return a function object for use

train_input_fn = make_input_fn(dftrain, y_train)  # here we will call the input_function that was returned to us to get a dataset object we can feed to the model
eval_input_fn = make_input_fn(dfeval, y_eval, num_epochs=1, shuffle=False)

# Eingabeschicht erstellen
inputs = {name: tf.keras.Input(shape=(1,), name=name, dtype='string' if dftrain[name].dtype == 'object' else 'float32') for name in dftrain.columns}
encoded_features = [preprocessor(inputs[name]) for name, preprocessor in feature_preprocessors.items()]
x = tf.keras.layers.concatenate(encoded_features)

# Einfaches lineares Modell
output = tf.keras.layers.Dense(1, activation='sigmoid')(x)
first_model = tf.keras.Model(inputs=inputs, outputs=output)

# Modell kompilieren und trainieren
first_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
first_model.fit(train_input_fn(), epochs=10)

# Modell auswerten
result = first_model.evaluate(eval_input_fn())
print(f"Accuracy: {result[1]}")
print(result)

id = 2
result = first_model.predict(eval_input_fn())
print(dfeval.loc[id])
print(y_eval[id])
print(result[id])