Damian89

import pandas

# Einlesen der Datei
training_data = pandas.read_csv("data/numerai_training_data.csv")

# Die ersten 5 Zeilen samt Header ausgeben
print(training_data.head())

# Aus wie vielen Zeilen und Spalten besteht die Datei?
print(training_data.shape)

Damian89

#!/usr/bin/env python
# coding: utf8

""" Example for numer.ai competition """

import math
import os
import sys

import numpy

Damian89

import warnings

from sklearn import ensemble

warnings.filterwarnings("ignore")

features = [
    [1, 1, 0],
    [1, 1, 0],
    [1, 1, 0],

Damian89

print()
print("TF-IDF + IncrementalPCA")

tfidf_vect = feature_extraction.text.TfidfVectorizer(stop_words=stoplist)
tfidf_vect.fit(docs)
features = tfidf_vect.get_feature_names()

tfidf = tfidf_vect.transform(docs)
tfidf_lsi = decomposition.IncrementalPCA(n_components=2)
tfidf_lsi.fit(tfidf.toarray())

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print()
print("TF-IDF + FastICA")

tfidf_vect = feature_extraction.text.TfidfVectorizer(stop_words=stoplist)
tfidf_vect.fit(docs)
features = tfidf_vect.get_feature_names()

tfidf = tfidf_vect.transform(docs)
tfidf_lsi = decomposition.FastICA(n_components=2)
tfidf_lsi.fit(tfidf.toarray())

Damian89

print()
print("TF-IDF + NMF")

tfidf_vect = feature_extraction.text.TfidfVectorizer(stop_words=stoplist)
tfidf_vect.fit(docs)
features = tfidf_vect.get_feature_names()

tfidf = tfidf_vect.transform(docs)
tfidf_lsi = decomposition.NMF(n_components=2)
tfidf_lsi.fit(tfidf)

Damian89

print("TF-IDF + LDA")

tfidf_vect = feature_extraction.text.TfidfVectorizer(stop_words=stoplist)
tfidf_vect.fit(docs)
features = tfidf_vect.get_feature_names()

tfidf = tfidf_vect.transform(docs)
tfidf_lsi = decomposition.LatentDirichletAllocation(n_components=2)
tfidf_lsi.fit(tfidf)

Damian89

#!/usr/bin/env python
# coding: utf8

from sklearn import feature_extraction, decomposition

stoplist = []

docs = [
    "Maschinelles lernen ist eine Disziplien die irgendwas mit Künstlicher Intelligenz zu tun hat",
    "Künstliche Intelligenz ist ein interessantes Themengebiet",