{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import newsource.libword as libword\n", "import math\n", "import pickle\n", "from decimal import *\n", "import pandas as pd\n", "from collections import OrderedDict as odict\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import importlib\n", "importlib.reload(libword)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "with open('wordDfs.pickle', 'rb') as fp:\n", " wordDfs = pickle.load(fp)\n", "with open('discretizeDfs.pickle', 'rb') as fp:\n", " discretizeDfs = pickle.load(fp)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "analisi = \"tf\"\n", "coord = 'X'\n", "csv = 1" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "if analisi == 'tf':\n", " dfs = libword.parallel(target=libword.extract_words, iterable=wordDfs, \n", " how=libword.text_freq, nwords=10, n=12)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "if analisi == 'idf':\n", " idfs = libword.idf(wordDfs.values())\n", " dfs = sorted(idfs, key=lambda x : idfs[x])[-10:]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "if analisi == 'idf2':\n", " g_idfs = libword.idf(g1.values())\n", " dfs = sorted(g_idfs, key=lambda x : g_idfs[x])[-10:]\n", " " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "if analisi == 'tf_idf':\n", " dfs = libword.tfidf(wordDfs, nwords=10)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "if analisi == 'tf_idf2':\n", " dfs = libword.tfidf(g, nwords=10)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "#x = odict((k,v) for k,v in dfs.items() if k[0] == 'X' )\n", "#y = odict((k,v) for k,v in dfs.items() if k[0] == 'Y' )\n", "#z = odict((k,v) for k,v in dfs.items() if k[0] == 'Z' )\n", "#w = odict((k,v) for k,v in dfs.items() if k[0] == 'W' )" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "#v = oc.image(wordDfs[('X',1)])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(40, 20)\n" ] }, { "data": { "text/html": [ "
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0.58036 -0.166630 -1.000000 \n", "30 -0.409930 0.217690 -0.028545 0.58036 -0.182790 -1.000000 \n", "31 -0.318920 0.151140 -0.003165 0.58036 0.512240 -1.000000 \n", "32 -0.292210 0.060722 0.054223 0.58036 0.580360 -1.000000 \n", "33 -0.292210 -0.054223 0.409930 0.58036 0.512240 -1.000000 \n", "34 -0.128920 -0.107670 0.580360 0.58036 0.580360 -1.000000 \n", "35 0.067262 -0.246940 0.580360 0.58036 0.580360 -1.000000 \n", "36 0.280020 -0.318920 0.580360 0.58036 0.580360 -1.000000 \n", "37 0.580360 -0.333750 0.580360 0.58036 0.580360 -1.000000 \n", "38 0.468980 -0.367590 0.580360 0.58036 0.580360 -1.000000 \n", "39 0.580360 -0.349870 0.580360 0.58036 0.580360 -1.000000 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = discretizeDfs[(coord,csv)]\n", "df = df.apply(lambda x: x.astype(float))\n", "print(df.shape)\n", "df\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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