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curl -fsSL https://gist.githubusercontent.com/satomacoto/b27dc3f871938d2614819bba3c589b45/raw/setup1.sh | bash
curl -fsSL https://gist.githubusercontent.com/satomacoto/b27dc3f871938d2614819bba3c589b45/raw/setup2.sh | bash

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pyenv install miniconda3-latest
pyenv local miniconda3-latest
conda create -n myenv anaconda
source (conda info --root)/etc/fish/conf.d/conda.fish
conda activate myenv

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# create an account, create an app
# @see https://apps.twitter.com/

# retrieve the access tokens
# @see https://dev.twitter.com/oauth/reference/post/oauth2/token

# create the file ~/twitter_api
nano ~/twitter_api
Authorization: OAuth oauth_consumer_key="XXXXXX", oauth_nonce="11111111", oauth_signature="XXXXXX", oauth_signature_method="HMAC-SHA1", oauth_timestamp="1450728725", oauth_token="99999-XXXXXX", oauth_version="1.0"

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機械学習ライブラリを利用するとき、モデルやパラメータの良し悪しを判断するための評価基準。

• 3.3. Model evaluation_ quantifying the quality of predictions — scikit-learn 0.17.1 documentation
• 3.5. Validation curves_ plotting scores to evaluate models — scikit-learn 0.17.1 documentation
• Machine Learning with Scikit Learn (Part II) - 人工知能に関する断創録

4.1 Cross validation

4.2 Model Complexity and GridSearchCV

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{"nodes":[{"name":"Myriel","group":1},{"name":"Napoleon","group":1},{"name":"Mlle.Baptistine","group":1},{"name":"Mme.Magloire","group":1},{"name":"CountessdeLo","group":1},{"name":"Geborand","group":1},{"name":"Champtercier","group":1},{"name":"Cravatte","group":1},{"name":"Count","group":1},{"name":"OldMan","group":1},{"name":"Labarre","group":2},{"name":"Valjean","group":2},{"name":"Marguerite","group":3},{"name":"Mme.deR","group":2},{"name":"Isabeau","group":2},{"name":"Gervais","group":2},{"name":"Tholomyes","group":3},{"name":"Listolier","group":3},{"name":"Fameuil","group":3},{"name":"Blacheville","group":3},{"name":"Favourite","group":3},{"name":"Dahlia","group":3},{"name":"Zephine","group":3},{"name":"Fantine","group":3},{"name":"Mme.Thenardier","group":4},{"name":"Thenardier","group":4},{"name":"Cosette","group":5},{"name":"Javert","group":4},{"name":"Fauchelevent","group":0},{"name":"Bamatabois","group":2},{"name":"Perpetue","group":3},{"name":"Simplice","group":2},{"name":"Scaufflaire","group":2},

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# -*- coding: utf-8 -*-

from sklearn.base import BaseEstimator, RegressorMixin, TransformerMixin
from sklearn.cross_validation import cross_val_predict


class ThroughTransformer(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None, **fit_param):
        return self

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Install pyenv

$ brew update
$ brew install pyenv

Check available versions and install

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class Pinv2Regressor:
    def __init__(self, estimators, cv=5):
        self.estimators = estimators
        self.cv = cv

    def fit(self, X, y, verbose=False):
        preds = {}
        for k, estimator in self.estimators.items():
            if verbose:
                print(k, estimator)

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{
	"build_systems":
	[
		{
			"file_regex": "^[ ]*File \"(...*?)\", line ([0-9]*)",
			"name": "Anaconda Python Builder",
			"selector": "source.python",
			"shell_cmd": "~/.virtualenvs/tensorflow/bin/python -u \"$file\""
		}
	],

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# -*- coding: utf-8 -*-


import gensim
from sklearn.feature_extraction.text import CountVectorizer
from gensim.corpora.dictionary import Dictionary
from gensim.corpora import MmCorpus
import numpy as np
import lda