From b9ace4871d33e72b6bcae627c32b67cc13323edd Mon Sep 17 00:00:00 2001
From: Denis Arrivault <denis.arrivault@lif.univ-mrs.fr>
Date: Tue, 6 Feb 2018 16:04:38 +0100
Subject: [PATCH] Code refactoring : removing Learning class
---
.gitignore | 1 +
examples/3.pautomac_light.train.dot.gv.pdf | Bin 14746 -> 0 bytes
...train.dot.gv => 3.pautomac_light.train.gv} | 11 +-
examples/3.pautomac_light.train.gv.pdf | Bin 0 -> 14552 bytes
examples/GraphvizTests.ipynb | 707 +++---------------
.../graphivz-output/3.pautomac_light.train.gv | 48 --
.../3.pautomac_light.train.gv.pdf | Bin 14746 -> 0 bytes
.../3.pautomac_light_man.train.gv.pdf | Bin 14746 -> 0 bytes
examples/graphivz-output/round-table.gv | 9 -
examples/graphivz-output/round-table.gv.pdf | Bin 15059 -> 0 bytes
splearn/__init__.py | 2 +-
splearn/automaton.py | 49 +-
splearn/hankel.py | 116 ++-
splearn/spectral.py | 269 +------
.../tests/datasets/3.pautomac_light.train.gv | 13 +-
splearn/tests/test_automaton.py | 21 +-
splearn/tests/test_hankel.py | 2 +-
splearn/tests/test_spectral.py | 41 +-
18 files changed, 320 insertions(+), 969 deletions(-)
delete mode 100644 examples/3.pautomac_light.train.dot.gv.pdf
rename examples/{3.pautomac_light.train.dot.gv => 3.pautomac_light.train.gv} (91%)
create mode 100644 examples/3.pautomac_light.train.gv.pdf
delete mode 100644 examples/graphivz-output/3.pautomac_light.train.gv
delete mode 100644 examples/graphivz-output/3.pautomac_light.train.gv.pdf
delete mode 100644 examples/graphivz-output/3.pautomac_light_man.train.gv.pdf
delete mode 100644 examples/graphivz-output/round-table.gv
delete mode 100644 examples/graphivz-output/round-table.gv.pdf
rename examples/graphivz-output/3.pautomac_light_man.train.gv => splearn/tests/datasets/3.pautomac_light.train.gv (89%)
diff --git a/.gitignore b/.gitignore
index 660e147..a5346ff 100644
--- a/.gitignore
+++ b/.gitignore
@@ -74,6 +74,7 @@ bin/*
.coverage
.cache
+uml*
skgilearn.egg-info/*
htmlcov/
doc/_build/
diff --git a/examples/3.pautomac_light.train.dot.gv.pdf b/examples/3.pautomac_light.train.dot.gv.pdf
deleted file mode 100644
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diff --git a/examples/3.pautomac_light.train.dot.gv b/examples/3.pautomac_light.train.gv
similarity index 91%
rename from examples/3.pautomac_light.train.dot.gv
rename to examples/3.pautomac_light.train.gv
index c3ac1dc..630afa0 100644
--- a/examples/3.pautomac_light.train.dot.gv
+++ b/examples/3.pautomac_light.train.gv
@@ -1,24 +1,17 @@
//3.pautomac_light.train.dot
digraph {
0 [label="0
-______
-> -0.00
-0.08 >"]
+______"]
1 [label="1
-______
-> 0.00
--0.02 >"]
+______"]
2 [label="2
______
-> -0.04
-0.45 >"]
3 [label="3
______
-> -0.11
0.63 >"]
4 [label="4
______
-> -0.09
-0.55 >"]
0 -> 1 [label="0:-0.24"]
0 -> 2 [label="0:0.35"]
diff --git a/examples/3.pautomac_light.train.gv.pdf b/examples/3.pautomac_light.train.gv.pdf
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diff --git a/examples/GraphvizTests.ipynb b/examples/GraphvizTests.ipynb
index e5b19df..e4786fd 100644
--- a/examples/GraphvizTests.ipynb
+++ b/examples/GraphvizTests.ipynb
@@ -1,106 +1,9 @@
{
"cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from graphviz import Digraph\n",
- "from graphviz import Source\n",
- "from splearn.datasets.base import load_data_sample\n",
- "from splearn.tests.datasets.get_dataset_path import get_dataset_path"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "train_file = '3.pautomac_light.train'\n",
- "data = load_data_sample(adr=get_dataset_path(train_file))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "4"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data.nbL"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "5000"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data.nbEx"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Splearn_array([[ 3., 0., 3., ..., -1., -1., -1.],\n",
- " [ 3., 3., -1., ..., -1., -1., -1.],\n",
- " [ 3., 2., 0., ..., -1., -1., -1.],\n",
- " ...,\n",
- " [ 3., 1., 3., ..., -1., -1., -1.],\n",
- " [ 3., 0., 3., ..., -1., -1., -1.],\n",
- " [ 3., 3., 1., ..., -1., -1., -1.]])"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data.data"
- ]
- },
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
- "outputs": [],
- "source": [
- "from splearn import Learning, Hankel , Spectral"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -112,346 +15,6 @@
"End of Automaton computation\n"
]
},
- {
- "data": {
- "text/plain": [
- "Spectral(lcolumns=7, lrows=7, mode_quiet=False, partial=True, rank=5,\n",
- " smooth_method='none', sparse=True, version='classic')"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "sp = Spectral()\n",
- "sp.fit(X=data.data)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "A = sp.Automaton"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([-0.00049344, 0.00306347, -0.04407393, -0.10777703, -0.08663914])"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "A.initial"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 0.07757137, -0.02422029, -0.44681254, 0.62773208, -0.55467443])"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "A.final"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[array([[ 0.04512121, -0.2403897 , 0.34945 , -0.28116807, -0.21402523],\n",
- " [ 0.06925801, -0.30062293, 0.20641375, -0.14960814, -0.55805732],\n",
- " [ 0.02980115, -0.13866481, 0.18362213, -0.20969545, -0.14481622],\n",
- " [ 0.00569934, -0.02338583, -0.06600665, 0.10749935, -0.15103655],\n",
- " [-0.02008655, 0.09026348, -0.00552559, -0.03135532, 0.24329022]]),\n",
- " array([[ 0.07744772, 0.09007074, -0.30472201, 0.27676245, 0.20289396],\n",
- " [-0.0990298 , -0.08061847, 0.25853171, -0.1208633 , -0.11085208],\n",
- " [-0.06171079, -0.06244152, 0.12007655, 0.00250637, -0.15679675],\n",
- " [-0.00273697, -0.00900572, -0.00046003, -0.00855043, -0.05375465],\n",
- " [ 0.03098733, 0.0397268 , -0.04997113, 0.00357694, 0.14182576]]),\n",
- " array([[-0.06791915, -0.11357938, 0.37955393, -0.2178498 , -0.22977695],\n",
- " [ 0.11596642, 0.14914957, -0.13357508, -0.00891606, 0.34841537],\n",
- " [ 0.01173082, 0.0192738 , 0.04142658, -0.03534659, 0.02316491],\n",
- " [ 0.00732891, 0.00553651, -0.02245608, 0.03611543, -0.03851434],\n",
- " [-0.01058989, -0.01062662, -0.00054311, -0.02556748, 0.04984889]]),\n",
- " array([[ 0.07276211, -0.01571956, 0.07428593, -0.10369862, 0.02475347],\n",
- " [-0.05607105, -0.08896207, 0.27638225, -0.23711256, 0.07372294],\n",
- " [-0.00739129, -0.0487418 , -0.62912397, 0.46816277, 0.09251699],\n",
- " [-0.00711022, -0.05623318, -0.36606659, -0.0132978 , 0.64910332],\n",
- " [ 0.00233552, -0.02156115, 0.09096243, -0.38438823, 0.66164772]])]"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "A.transitions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'classic'"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "A.type"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "4"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "A.nbL"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "5"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "A.nbS"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "th = 0.2\n",
- "wa = Digraph(comment='3.pautomac_light.train')\n",
- "for i in range(A.nbS):\n",
- " label = \"{0:d}\\n______\\n> {1:.2f}\\n{2:.2f} >\".format(i, A.initial[i], A.final[i])\n",
- " wa.node(str(i),label)\n",
- "for l in range(A.nbL):\n",
- " for i in range(A.nbS):\n",
- " for j in range(A.nbS):\n",
- " weight = A.transitions[l][i,j]\n",
- " if (np.abs(weight) > th):\n",
- " label = \"{0:d}:{1:.2f}\".format(l,weight)\n",
- " wa.edge(str(i), str(j), label)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "// 3.pautomac_light.train\n",
- "digraph {\n",
- "\t0 [label=\"0\n",
- "______\n",
- "> -0.00\n",
- "0.08 >\"]\n",
- "\t1 [label=\"1\n",
- "______\n",
- "> 0.00\n",
- "-0.02 >\"]\n",
- "\t2 [label=\"2\n",
- "______\n",
- "> -0.04\n",
- "-0.45 >\"]\n",
- "\t3 [label=\"3\n",
- "______\n",
- "> -0.11\n",
- "0.63 >\"]\n",
- "\t4 [label=\"4\n",
- "______\n",
- "> -0.09\n",
- "-0.55 >\"]\n",
- "\t0 -> 1 [label=\"0:-0.24\"]\n",
- "\t0 -> 2 [label=\"0:0.35\"]\n",
- "\t0 -> 3 [label=\"0:-0.28\"]\n",
- "\t0 -> 4 [label=\"0:-0.21\"]\n",
- "\t1 -> 1 [label=\"0:-0.30\"]\n",
- "\t1 -> 2 [label=\"0:0.21\"]\n",
- "\t1 -> 4 [label=\"0:-0.56\"]\n",
- "\t2 -> 3 [label=\"0:-0.21\"]\n",
- "\t4 -> 4 [label=\"0:0.24\"]\n",
- "\t0 -> 2 [label=\"1:-0.30\"]\n",
- "\t0 -> 3 [label=\"1:0.28\"]\n",
- "\t0 -> 4 [label=\"1:0.20\"]\n",
- "\t1 -> 2 [label=\"1:0.26\"]\n",
- "\t0 -> 2 [label=\"2:0.38\"]\n",
- "\t0 -> 3 [label=\"2:-0.22\"]\n",
- "\t0 -> 4 [label=\"2:-0.23\"]\n",
- "\t1 -> 4 [label=\"2:0.35\"]\n",
- "\t1 -> 2 [label=\"3:0.28\"]\n",
- "\t1 -> 3 [label=\"3:-0.24\"]\n",
- "\t2 -> 2 [label=\"3:-0.63\"]\n",
- "\t2 -> 3 [label=\"3:0.47\"]\n",
- "\t3 -> 2 [label=\"3:-0.37\"]\n",
- "\t3 -> 4 [label=\"3:0.65\"]\n",
- "\t4 -> 3 [label=\"3:-0.38\"]\n",
- "\t4 -> 4 [label=\"3:0.66\"]\n",
- "}\n"
- ]
- }
- ],
- "source": [
- "print(wa.source)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "//automata.dot\n",
- "digraph {\n",
- "\t0 [label=\"0\n",
- "______\n",
- "> -0.00\n",
- "0.08 >\"]\n",
- "\t1 [label=\"1\n",
- "______\n",
- "> 0.00\n",
- "-0.02 >\"]\n",
- "\t2 [label=\"2\n",
- "______\n",
- "> -0.04\n",
- "-0.45 >\"]\n",
- "\t3 [label=\"3\n",
- "______\n",
- "> -0.11\n",
- "0.63 >\"]\n",
- "\t4 [label=\"4\n",
- "______\n",
- "> -0.09\n",
- "-0.55 >\"]\n",
- "\t0 -> 1 [label=\"0:-0.24\"]\n",
- "\t0 -> 2 [label=\"0:0.35\"]\n",
- "\t0 -> 3 [label=\"0:-0.28\"]\n",
- "\t0 -> 4 [label=\"0:-0.21\"]\n",
- "\t1 -> 1 [label=\"0:-0.30\"]\n",
- "\t1 -> 2 [label=\"0:0.21\"]\n",
- "\t1 -> 4 [label=\"0:-0.56\"]\n",
- "\t2 -> 3 [label=\"0:-0.21\"]\n",
- "\t4 -> 4 [label=\"0:0.24\"]\n",
- "\t0 -> 2 [label=\"1:-0.30\"]\n",
- "\t0 -> 3 [label=\"1:0.28\"]\n",
- "\t0 -> 4 [label=\"1:0.20\"]\n",
- "\t1 -> 2 [label=\"1:0.26\"]\n",
- "\t0 -> 2 [label=\"2:0.38\"]\n",
- "\t0 -> 3 [label=\"2:-0.22\"]\n",
- "\t0 -> 4 [label=\"2:-0.23\"]\n",
- "\t1 -> 4 [label=\"2:0.35\"]\n",
- "\t1 -> 2 [label=\"3:0.28\"]\n",
- "\t1 -> 3 [label=\"3:-0.24\"]\n",
- "\t2 -> 2 [label=\"3:-0.63\"]\n",
- "\t2 -> 3 [label=\"3:0.47\"]\n",
- "\t3 -> 2 [label=\"3:-0.37\"]\n",
- "\t3 -> 4 [label=\"3:0.65\"]\n",
- "\t4 -> 3 [label=\"3:-0.38\"]\n",
- "\t4 -> 4 [label=\"3:0.66\"]\n",
- "}\n",
- "\n"
- ]
- }
- ],
- "source": [
- "nb_dec = 2\n",
- "threshold = th\n",
- "filename = \"automata.dot\"\n",
- "prec = \".{:d}f\".format(nb_dec)\n",
- "out = \"//{:s}\\ndigraph {{\\n\".format(filename)\n",
- "for i in range(A.nbS):\n",
- " label = \"{0:d}\\n______\\n> {1:\" + prec + \"}\\n{2:\" + prec + \"} >\"\n",
- " label = label.format(i, A.initial[i], A.final[i])\n",
- " out += \"\\t{0:d} [label=\\\"\".format(i)\n",
- " out += label + \"\\\"]\\n\"\n",
- "for l in range(A.nbL):\n",
- " for i in range(A.nbS):\n",
- " for j in range(A.nbS):\n",
- " weight = A.transitions[l][i,j]\n",
- " if (np.abs(weight) > threshold):\n",
- " label = \"{0:d}:{1:\" + prec + \"}\"\n",
- " label = label.format(l,weight)\n",
- " out += \"\\t{0:d} -> {1:d} [label=\\\"\".format(i,j)\n",
- " out += label + \"\\\"]\\n\"\n",
- "out += \"}\\n\"\n",
- "print(out)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
{
"data": {
"image/svg+xml": [
@@ -461,269 +24,202 @@
"<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n",
" -->\n",
"<!-- Title: %3 Pages: 1 -->\n",
- "<svg width=\"769pt\" height=\"693pt\"\n",
- " viewBox=\"0.00 0.00 769.23 692.83\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
- "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 688.833)\">\n",
+ "<svg width=\"779pt\" height=\"544pt\"\n",
+ " viewBox=\"0.00 0.00 778.50 544.34\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+ "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 540.34)\">\n",
"<title>%3</title>\n",
- "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-688.833 765.226,-688.833 765.226,4 -4,4\"/>\n",
+ "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-540.34 774.504,-540.34 774.504,4 -4,4\"/>\n",
"<!-- 0 -->\n",
"<g id=\"node1\" class=\"node\"><title>0</title>\n",
- "<ellipse fill=\"none\" stroke=\"black\" cx=\"454.226\" cy=\"-636.749\" rx=\"40.1111\" ry=\"48.1667\"/>\n",
- "<text text-anchor=\"middle\" x=\"454.226\" y=\"-655.549\" font-family=\"Times,serif\" font-size=\"14.00\">0</text>\n",
- "<text text-anchor=\"middle\" x=\"454.226\" y=\"-640.549\" font-family=\"Times,serif\" font-size=\"14.00\">______</text>\n",
- "<text text-anchor=\"middle\" x=\"454.226\" y=\"-625.549\" font-family=\"Times,serif\" font-size=\"14.00\">> -0.00</text>\n",
- "<text text-anchor=\"middle\" x=\"454.226\" y=\"-610.549\" font-family=\"Times,serif\" font-size=\"14.00\">0.08 ></text>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"468.504\" cy=\"-509.47\" rx=\"40.1111\" ry=\"26.7407\"/>\n",
+ "<text text-anchor=\"middle\" x=\"468.504\" y=\"-513.27\" font-family=\"Times,serif\" font-size=\"14.00\">0</text>\n",
+ "<text text-anchor=\"middle\" x=\"468.504\" y=\"-498.27\" font-family=\"Times,serif\" font-size=\"14.00\">______</text>\n",
"</g>\n",
"<!-- 1 -->\n",
"<g id=\"node2\" class=\"node\"><title>1</title>\n",
- "<ellipse fill=\"none\" stroke=\"black\" cx=\"91.2259\" cy=\"-489.583\" rx=\"40.1111\" ry=\"48.1667\"/>\n",
- "<text text-anchor=\"middle\" x=\"91.2259\" y=\"-508.383\" font-family=\"Times,serif\" font-size=\"14.00\">1</text>\n",
- "<text text-anchor=\"middle\" x=\"91.2259\" y=\"-493.383\" font-family=\"Times,serif\" font-size=\"14.00\">______</text>\n",
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- "<text text-anchor=\"middle\" x=\"91.2259\" y=\"-463.383\" font-family=\"Times,serif\" font-size=\"14.00\">-0.02 ></text>\n",
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+ "<text text-anchor=\"middle\" x=\"104.504\" y=\"-408.53\" font-family=\"Times,serif\" font-size=\"14.00\">1</text>\n",
+ "<text text-anchor=\"middle\" x=\"104.504\" y=\"-393.53\" font-family=\"Times,serif\" font-size=\"14.00\">______</text>\n",
"</g>\n",
"<!-- 0->1 -->\n",
"<g id=\"edge1\" class=\"edge\"><title>0->1</title>\n",
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- "<polygon fill=\"black\" stroke=\"black\" points=\"127.545,-530.542 118.221,-525.509 122.371,-535.257 127.545,-530.542\"/>\n",
- "<text text-anchor=\"middle\" x=\"191.226\" y=\"-559.466\" font-family=\"Times,serif\" font-size=\"14.00\">0:-0.24</text>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M427.911,-507.623C372.424,-505.241 270.643,-496.46 190.504,-464.6 171.21,-456.93 151.934,-444.308 136.536,-432.714\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"138.454,-429.773 128.405,-426.417 134.168,-435.307 138.454,-429.773\"/>\n",
+ "<text text-anchor=\"middle\" x=\"208.004\" y=\"-453.4\" font-family=\"Times,serif\" font-size=\"14.00\">0:0.24</text>\n",
"</g>\n",
"<!-- 2 -->\n",
"<g id=\"node3\" class=\"node\"><title>2</title>\n",
- "<ellipse fill=\"none\" stroke=\"black\" cx=\"271.226\" cy=\"-342.416\" rx=\"40.1111\" ry=\"48.1667\"/>\n",
- "<text text-anchor=\"middle\" x=\"271.226\" y=\"-361.216\" font-family=\"Times,serif\" font-size=\"14.00\">2</text>\n",
- "<text text-anchor=\"middle\" x=\"271.226\" y=\"-346.216\" font-family=\"Times,serif\" font-size=\"14.00\">______</text>\n",
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+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"285.504\" cy=\"-289.383\" rx=\"40.1111\" ry=\"37.4533\"/>\n",
+ "<text text-anchor=\"middle\" x=\"285.504\" y=\"-300.683\" font-family=\"Times,serif\" font-size=\"14.00\">2</text>\n",
+ "<text text-anchor=\"middle\" x=\"285.504\" y=\"-285.683\" font-family=\"Times,serif\" font-size=\"14.00\">______</text>\n",
+ "<text text-anchor=\"middle\" x=\"285.504\" y=\"-270.683\" font-family=\"Times,serif\" font-size=\"14.00\">-0.45 ></text>\n",
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],
"text/plain": [
- "<graphviz.files.Source at 0x7f25558204a8>"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "wa.render('graphivz-output/3.pautomac_light.train.gv', view=False)\n",
- "Source.from_file(\"graphivz-output/3.pautomac_light.train.gv\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "src = Source(out)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'graphivz-output/3.pautomac_light_man.train.gv.pdf'"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "src.render('graphivz-output/3.pautomac_light_man.train.gv', view=True) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Start Hankel matrix computation\n",
- "End of Hankel matrix computation\n",
- "Start Building Automaton from Hankel matrix\n",
- "End of Automaton computation\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'3.pautomac_light.train.dot.gv.pdf'"
+ "<graphviz.files.Source at 0x7f99d48d7630>"
]
},
- "execution_count": 1,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -737,11 +233,12 @@
"sp = Spectral()\n",
"sp.fit(X=data.data)\n",
"dotfile = \"3.pautomac_light.train.dot\"\n",
- "dot = sp.Automaton.get_dot(threshold = 0.2, title = dotfile)\n",
+ "dot = sp.automaton.get_dot(threshold = 0.2, title = dotfile)\n",
"# To display the dot string one can use graphviz:\n",
"from graphviz import Source\n",
"src = Source(dot)\n",
- "src.render(dotfile + '.gv', view=True) "
+ "src.render(train_file + '.gv', view=False)\n",
+ "Source.from_file(train_file + '.gv')"
]
},
{
diff --git a/examples/graphivz-output/3.pautomac_light.train.gv b/examples/graphivz-output/3.pautomac_light.train.gv
deleted file mode 100644
index 4e539a2..0000000
--- a/examples/graphivz-output/3.pautomac_light.train.gv
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diff --git a/examples/graphivz-output/3.pautomac_light.train.gv.pdf b/examples/graphivz-output/3.pautomac_light.train.gv.pdf
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diff --git a/examples/graphivz-output/3.pautomac_light_man.train.gv.pdf b/examples/graphivz-output/3.pautomac_light_man.train.gv.pdf
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diff --git a/examples/graphivz-output/round-table.gv b/examples/graphivz-output/round-table.gv
deleted file mode 100644
index 48aa015..0000000
--- a/examples/graphivz-output/round-table.gv
+++ /dev/null
@@ -1,9 +0,0 @@
-// The Round Table
-digraph {
- A [label="King Arthur"]
- B [label="Sir Bedevere the Wise"]
- L [label="Sir Lancelot the Brave"]
- A -> B
- A -> L
- B -> L [constraint=false]
-}
diff --git a/examples/graphivz-output/round-table.gv.pdf b/examples/graphivz-output/round-table.gv.pdf
deleted file mode 100644
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diff --git a/splearn/__init__.py b/splearn/__init__.py
index 384681e..88d3dc0 100644
--- a/splearn/__init__.py
+++ b/splearn/__init__.py
@@ -1,5 +1,5 @@
from splearn.automaton import Automaton
-from splearn.spectral import Learning
+#from splearn.spectral import Learning
from splearn.spectral import Spectral
from splearn.hankel import Hankel
__version__ = "1.1.0"
diff --git a/splearn/automaton.py b/splearn/automaton.py
index 44338b9..f5a09fe 100644
--- a/splearn/automaton.py
+++ b/splearn/automaton.py
@@ -4,7 +4,6 @@
import numpy as np
-
class Automaton(object):
""" Define an automaton with parameters
@@ -149,7 +148,7 @@ class Automaton(object):
raise TypeError(mess)
if (type_value == 'classic' or type_value == 'prefix' or
type_value == 'suffix' or type_value == 'factor'):
- self._type = type_value
+ self._type = type_value
else:
raise ValueError("type must be classic, prefix, suffix or factor.")
@@ -286,6 +285,26 @@ class Automaton(object):
lh[x+1][i, j] = self.val(w)
dval[w] = lh[x+1][i, j]
return lh
+
+ def to_hankel(self, lrows, lcolumns, mode_quiet=False):
+ """ Return an Hankel instance (denses, classic and not partial) with matrices
+ built on lrows and lcolumns from an automaton
+
+ - Input:
+
+ :param list lrows: prefixes
+ :param list lcolumns: suffixes
+ :param boolean mode_quiet: (default value = False) True for no
+ output message.
+
+ - Output:
+
+ :returns: Hankel instance
+ :rtype: Hankel
+ """
+ from splearn.hankel import Hankel
+ lhankels = self.BuildHankels(lrows, lcolumns)
+ return Hankel(mode_quiet=mode_quiet, lhankels=lhankels, rank=self.nbS)
def mirror(self):
""" Compute the mirror automaton
@@ -637,15 +656,31 @@ class Automaton(object):
prec = ".{:d}f".format(nb_dec)
out = "//{:s}\ndigraph {{\n".format(title)
for i in range(self.nbS):
- label = "{0:d}\n______\n> {1:" + prec + "}\n{2:" + prec + "} >"
- label = label.format(i, self.initial[i], self.final[i])
- out += "\t{0:d} [label=\"".format(i)
- out += label + "\"]\n"
+ if np.abs(self.initial[i]) > threshold and np.abs(self.final[i]) > threshold:
+ label = "{0:d}\n______\n> {1:" + prec + "}\n{2:" + prec + "} >"
+ label = label.format(i, self.initial[i], self.final[i])
+ out += "\t{0:d} [label=\"".format(i)
+ out += label + "\"]\n"
+ elif np.abs(self.initial[i]) > threshold:
+ label = "{0:d}\n______\n> {1:" + prec + "}"
+ label = label.format(i, self.initial[i])
+ out += "\t{0:d} [label=\"".format(i)
+ out += label + "\"]\n"
+ elif np.abs(self.final[i]) > threshold:
+ label = "{0:d}\n______\n{1:" + prec + "} >"
+ label = label.format(i, self.final[i])
+ out += "\t{0:d} [label=\"".format(i)
+ out += label + "\"]\n"
+ else:
+ label = "{0:d}\n______"
+ label = label.format(i)
+ out += "\t{0:d} [label=\"".format(i)
+ out += label + "\"]\n"
for l in range(self.nbL):
for i in range(self.nbS):
for j in range(self.nbS):
weight = self.transitions[l][i,j]
- if (np.abs(weight) > threshold):
+ if np.abs(weight) > threshold:
label = "{0:d}:{1:" + prec + "}"
label = label.format(l,weight)
out += "\t{0:d} -> {1:d} [label=\"".format(i,j)
diff --git a/splearn/hankel.py b/splearn/hankel.py
index b20f7bd..41d58ea 100644
--- a/splearn/hankel.py
+++ b/splearn/hankel.py
@@ -4,9 +4,9 @@
"""
from __future__ import division, print_function
import scipy.sparse as sps
+import scipy.sparse.linalg as lin
import numpy as np
-
class Hankel(object):
""" A Hankel instance , compute the list of Hankel matrices
@@ -25,7 +25,7 @@ class Hankel(object):
- Input:
- :param dict sample: sample dictionary
+ :param dict sample_instance: sample dictionary
:param dict pref: prefix dictionary
:param dict suff: suffix dictionary
:param dict fact: factor dictionary
@@ -47,26 +47,45 @@ class Hankel(object):
matrix is sparse
:param boolean mode_quiet: (default value = False) True for no
output message.
+ :param list of all Hankel matrices. At least one of the two parameters
+ *sample_instance* or *lhankels* has to be not None. If *sample_instance* is given,
+ the **Hankel** instance is built directly from the sample dictionnary,
+ else it is deduced from the *lhankels* list of matrices.
+ :param int rank: the rank of the Hankel matrices. Required if **Hankel** is build from *lhankels*.
"""
def __init__(
- self, sample_instance,
+ self, sample_instance=None,
lrows=[], lcolumns=[],
version="classic", partial=False,
- sparse=False, mode_quiet=False):
- # Size of the alphabet
- self.nbL = sample_instance.nbL
- # Number of samples
- self.nbEx = sample_instance.nbEx
+ sparse=False, mode_quiet=False, lhankels=None, rank = -1):
+
self.version = version
self.partial = partial
self.sparse = sparse
- self.lhankel = self.build(sample=sample_instance.sample,
- pref=sample_instance.pref,
- suff=sample_instance.suff,
- fact=sample_instance.fact,
- lrows=lrows, lcolumns=lcolumns,
- mode_quiet=mode_quiet)
+ self.buildFromSample = True
+ if sample_instance is not None:
+ # Size of the alphabet
+ self.nbL = sample_instance.nbL
+ # Number of samples
+ self.nbEx = sample_instance.nbEx
+ self.lhankel = self.build(sample=sample_instance.sample,
+ pref=sample_instance.pref,
+ suff=sample_instance.suff,
+ fact=sample_instance.fact,
+ lrows=lrows, lcolumns=lcolumns,
+ mode_quiet=mode_quiet)
+ elif lhankels is not None:
+ # Size of the alphabet
+ self.nbL = len(lhankels) - 1
+ # Number of samples
+ if rank == -1:
+ raise ValueError("Building an Hankel instance from list of hankel matrices required a valid rank.")
+ self.nbS = rank
+ self.lhankel = lhankels
+ self.buildFromSample = False
+ else:
+ raise ValueError("At least sample_instance or lhankel has to be not None.")
@property
def nbL(self):
@@ -245,3 +264,72 @@ class Hankel(object):
dcolumns = {lcolumns[i]: i for i in range(nbColumns)}
return (drows, dcolumns)
+
+ def to_automaton(self, rank, mode_quiet):
+ """ Return an automaton from the current Hankel matrix
+
+ - Input:
+
+ :param int rank: the ranking number
+ :param boolean mode_quiet: True for no output message.
+
+ - Output:
+
+ :returns: An automaton instance
+ :rtype: Automaton
+ """
+ from splearn.automaton import Automaton
+ if not mode_quiet:
+ print("Start Building Automaton from Hankel matrix")
+ matrix_shape =min(self.lhankel[0].shape)
+ if (min(self.lhankel[0].shape) < rank) :
+ raise ValueError("The value of parameter rank ("+ str(rank)
+ + ") should be less than " +
+ "the smaller dimension of the Hankel Matrix (" +
+ str(matrix_shape) + ")")
+ if not self.sparse:
+ hankel = self.lhankel[0]
+ [u, s, v] = np.linalg.svd(hankel)
+ u = u[:, :rank]
+ v = v[:rank, :]
+ # ds = np.zeros((rank, rank), dtype=complex)
+ ds = np.diag(s[:rank])
+ pis = np.linalg.pinv(v)
+ del v
+ pip = np.linalg.pinv(np.dot(u, ds))
+ del u, ds
+ init = np.dot(hankel[0, :], pis)
+ term = np.dot(pip, hankel[:, 0])
+ trans = []
+ for x in range(self.nbL):
+ hankel = self.lhankel[x+1]
+ trans.append(np.dot(pip, np.dot(hankel, pis)))
+
+ else:
+ hankel = self.lhankel[0]
+ [u, s, v] = lin.svds(hankel, k=rank)
+ ds = np.diag(s)
+ pis = np.linalg.pinv(v)
+ del v
+ pip = np.linalg.pinv(np.dot(u, ds))
+ del u, ds
+ init = hankel[0, :].dot(pis)[0, :]
+ term = np.dot(pip, hankel[:, 0].toarray())[:, 0]
+ trans = []
+ for x in range(self.nbL):
+ hankel = self.lhankel[x+1]
+ trans.append(np.dot(pip, hankel.dot(pis)))
+
+ A = Automaton(nbL=self.nbL, nbS=rank, initial=init, final=term,
+ transitions=trans, type=self.version)
+ if self.buildFromSample:
+ A.initial = A.initial / self.nbEx
+ if self.version == "prefix":
+ A = A.transformation(source="prefix", target="classic")
+ if self.version == "factor":
+ A = A.transformation(source="factor", target="classic")
+ if self.version == "suffix":
+ A = A.transformation(source="suffix", target="classic")
+ if not mode_quiet:
+ print ("End of Automaton computation")
+ return A
diff --git a/splearn/spectral.py b/splearn/spectral.py
index ce8e027..883ffdf 100644
--- a/splearn/spectral.py
+++ b/splearn/spectral.py
@@ -6,12 +6,9 @@
"""
from __future__ import division, print_function
import numpy as np
-import scipy.sparse as sps
-import scipy.sparse.linalg as lin
import math
from splearn.datasets.data_sample import Splearn_array
from splearn.hankel import Hankel
-from splearn.automaton import Automaton
from sklearn.base import BaseEstimator
from sklearn.utils import check_array
from sklearn.utils.validation import NotFittedError
@@ -33,7 +30,7 @@ class Spectral(BaseEstimator):
Start Building Automaton from Hankel matrix
End of Automaton computation
Spectral(lcolumns=6, lrows=6, partial=True, rank=5, smooth_method='trigram', sparse=True, version='classic')
- >>> sp.Automaton.initial
+ >>> sp.automaton.initial
array([-0.00049249, 0.00304676, -0.04405996, -0.10765322, -0.08660063])
>>> sp.predict(data.data)
array([ 4.38961058e-04, 1.10616861e-01, 1.35569353e-03, ...,
@@ -91,6 +88,8 @@ class Spectral(BaseEstimator):
self.smooth_method = smooth_method
self._rule_smooth_method(smooth_method)
self.mode_quiet = mode_quiet
+ self._automaton = None
+ self._hankel = None
def get_params(self, deep=True):
# suppose this estimator has parameters
@@ -110,6 +109,23 @@ class Spectral(BaseEstimator):
"smooth_method" : self.smooth_method,
"mode_quiet" : self.mode_quiet
}
+ @property
+ def automaton(self):
+ """Automaton build by the fit method. None by default"""
+ return self._automaton
+
+ @automaton.setter
+ def automaton(self, automaton):
+ pass
+
+ @property
+ def hankel(self):
+ """Hankel build by the fit method. None by default"""
+ return self._hankel
+
+ @hankel.setter
+ def hankel(self, hankel):
+ pass
def _rule_smooth_method(self, value):
if self.smooth_method not in ['none', 'trigram']:
@@ -157,16 +173,16 @@ class Spectral(BaseEstimator):
check_array(X)
if not isinstance(X, Splearn_array):
- self.Automaton = None
+ self._hankel = None
+ self._automaton = None
return self
X = self._polulate_dictionnaries(X)
- learn = Learning(data=X)
- self.Automaton = learn.LearnAutomaton(rank=self.rank,
- lrows=self.lrows, lcolumns=self.lcolumns,
- version=self.version,
- partial=self.partial,
- sparse=self.sparse,
- mode_quiet = self.mode_quiet)
+ self._hankel = Hankel(sample_instance=X,
+ lrows=self.lrows, lcolumns=self.lcolumns,
+ version=self.version,
+ partial=self.partial, sparse=self.sparse,
+ mode_quiet=self.mode_quiet)
+ self._automaton = self._hankel.to_automaton(self.rank, self.mode_quiet)
# for smooth option compute trigram dictionnary
if self.smooth == 1:
self.trigram = self._threegramdict(X.sample)
@@ -421,10 +437,13 @@ class Spectral(BaseEstimator):
"""
check_array(X)
- if not hasattr(self, 'Automaton'):
+ if not hasattr(self, 'automaton'):
raise NotFittedError("This %(name)s instance is not fitted "
"yet" % {'name': type(self).__name__})
- if self.Automaton is None:
+ if self._automaton is None:
+ print("No Automaton has been computed, "
+ "check the format of the input fit data")
+ warnings.warn("check the format of the input fit data", UserWarning)
return X
Y = self.predict_proba(X)
@@ -447,12 +466,12 @@ class Spectral(BaseEstimator):
"""
#check_is_fitted(self, "classes_")
X = check_array(X)
- if not hasattr(self, 'Automaton'):
+ if not hasattr(self, 'automaton'):
raise NotFittedError("This %(name)s instance is not fitted "
"yet" % {'name': type(self).__name__})
# if Automaton is None because the fit pass through doing nothing
- if self.Automaton is None:
+ if self._automaton is None:
print("No Automaton has been computed, "
"check the format of the input fit data")
warnings.warn("check the format of the input fit data", UserWarning)
@@ -472,10 +491,10 @@ class Spectral(BaseEstimator):
w = X[line, :]
w = w[w >= 0]
w = tuple([int(x) for x in w[0:]])
- val = self.Automaton.val(w)
+ val = self._automaton.val(w)
if self.smooth == 1 and val <= 0:
- Y[i] = self._trigramprobability(w, trigram_test)
- trigram_index[i] = True
+ Y[i] = self._trigramprobability(w, trigram_test)
+ trigram_index[i] = True
else:
Y[i] = val
i += 1
@@ -569,214 +588,4 @@ class Spectral(BaseEstimator):
raise ValueError(msg)
return perplexity
else:
- return - self.loss(X, y, normalize=True)
-
-class Learning(object):
- """ A learning instance
-
- :Example:
-
- >>> from splearn.spectral import Learning, Spectral
- >>> train_file = '0.spice.train'
- >>> from splearn.datasets.base import load_data_sample
- >>> from splearn.tests.datasets.get_dataset_path import get_dataset_path
- >>> train_file = '3.pautomac_light.train' # '4.spice.train'
- >>> data_sample = load_data_sample(adr=get_dataset_path(train_file))
- >>> from splearn.spectral import Learning, Spectral
- >>> cl = Spectral()
- >>> cl.set_params(partial=True, lcolumns=7, lrows=7)
- Spectral(lcolumns=[], lrows=[], partial=True, rank=5, sparse=False, version='classic')
- >>> cl.fit(data_sample.data)
- >>> S_app = Learning(data=data_sample.data)
-
- - Input:
-
- :param Splearn_array data: an instance of Splearn_array
- (ndarray, nbL, nbEx, and dictionaries )
-
- """
-
- def __init__(self, data):
-
- # # Size of the alphabet
- # self.nbL = sample_instance.nbL
- # # Number of samples
- # self.nbEx = sample_instance.nbEx
- # # The dictionary that contains the sample
- # self.sample = sample_instance.sample
- # # The dictionary that contains the prefixes
- # self.pref = sample_instance.pref
- # # The dictionary that contains the suffixes
- # self.suff = sample_instance.suff
- # # The dictionary that contains the factors
- # self.fact = sample_instance.fact
- # The pLearn that contains Samples dictionaries
- self.splearn_object = data
-
- @property
- def splearn_object(self):
- """Sample object, contains dictionaries"""
-
- return self._splearn_object
-
- @splearn_object.setter
- def splearn_object(self, splearn_object):
- if not isinstance(splearn_object, Splearn_array):
- raise TypeError("sample_object should be a Splearn_array")
- self._splearn_object = splearn_object
-
- @staticmethod
- def BuildAutomatonFromHankel(lhankel, nbL, rank, mode_quiet,
- sparse=True, version='classic',
- ):
- """ Build an automaton from Hankel matrix
-
- - Input:
-
- :param list lhankel: list of Hankel matrix
- :param int nbL: the number of letters
- :param int rank: the ranking number
- :param boolean sparse: (default value = False) True if Hankel
- matrix is sparse
- :param version: version of Automaton
- :param boolean mode_quiet: True for no output message.
-
- - Output:
-
- :returns: An automaton instance
- :rtype: Automaton
- """
- if not mode_quiet:
- print("Start Building Automaton from Hankel matrix")
- if not sparse:
- hankel = lhankel[0]
- [u, s, v] = np.linalg.svd(hankel)
- u = u[:, :rank]
- v = v[:rank, :]
- # ds = np.zeros((rank, rank), dtype=complex)
- ds = np.diag(s[:rank])
- pis = np.linalg.pinv(v)
- del v
- pip = np.linalg.pinv(np.dot(u, ds))
- del u, ds
- init = np.dot(hankel[0, :], pis)
- term = np.dot(pip, hankel[:, 0])
- trans = []
- for x in range(nbL):
- hankel = lhankel[x+1]
- trans.append(np.dot(pip, np.dot(hankel, pis)))
-
- else:
- hankel = lhankel[0]
- [u, s, v] = lin.svds(hankel, k=rank)
- ds = np.diag(s)
- pis = np.linalg.pinv(v)
- del v
- pip = np.linalg.pinv(np.dot(u, ds))
- del u, ds
- init = hankel[0, :].dot(pis)[0, :]
- term = np.dot(pip, hankel[:, 0].toarray())[:, 0]
- trans = []
- for x in range(nbL):
- hankel = lhankel[x+1]
- trans.append(np.dot(pip, hankel.dot(pis)))
-
- A = Automaton(nbL=nbL, nbS=rank, initial=init, final=term,
- transitions=trans, type=version)
- # ms=np.linalg.pinv(vt)
- # init=h.dot(ms)/self.nbEx
- # print(type(init),init.shape)
- # mp=np.linalg.pinv(u.dot(np.diag(s)))
- # v=v.todense()
- # term=np.dot(mp,v)
- # trans=[] # a suivre
- if not mode_quiet:
- print ("End of Automaton computation")
- return A
-
- def LearnAutomaton(self, rank, lrows=[], lcolumns=[], version="classic",
- partial=False, sparse=False,
- mode_quiet=False):
- """ Learn Automaton from sample
-
- - Input:
-
- :param int rank: the ranking number
- :param lrows: number or list of rows,
- a list of strings if partial=True;
- otherwise, based on self.pref if version="classic" or
- "prefix", self.fact otherwise
- :type lrows: int or list of int
- :param lcolumns: number or list of columns
- a list of strings if partial=True ;
- otherwise, based on self.suff if version="classic" or "suffix",
- self.fact otherwise
- :type lcolumns: int or list of int
- :param string version: (default = "classic") version name
- :param boolean partial: (default value = False) build
- of partial Hankel matrix
- :param boolean sparse: (default value = False) True if Hankel
- matrix is sparse
- :param boolean mode_quiet: (default value = False) True for no
- output message.
-
- - Output:
-
- :returns: An automaton instance
- :rtype: Automaton
-
- """
- lhankel = Hankel(sample_instance=self.splearn_object,
- lrows=lrows, lcolumns=lcolumns,
- version=version,
- partial=partial, sparse=sparse,
- mode_quiet=mode_quiet).lhankel
- matrix_shape =min(lhankel[0].shape)
- if (min(lhankel[0].shape) < rank) :
- raise ValueError("The value of parameter rank ("+ str(rank)
- + ") should be less than " +
- "the smaller dimension of the Hankel Matrix (" +
- str(matrix_shape) + ")")
- A = self.BuildAutomatonFromHankel(lhankel=lhankel,
- nbL=self.splearn_object.nbL,
- rank=rank,
- sparse=sparse,
- version=version,
- mode_quiet=mode_quiet)
-
-
- A.initial = A.initial / self.splearn_object.nbEx
- if version == "prefix":
- A = A.transformation(source="prefix", target="classic")
- if version == "factor":
- A = A.transformation(source="factor", target="classic")
- if version == "suffix":
- A = A.transformation(source="suffix", target="classic")
- return A
-
-# @staticmethod
-# def Perplexity(A, adr):
-# """ Perplexity calculation """
-# Cible = Automaton.load_Spice_Automaton("./" + adr + ".target")
-# Test = Learning(adr="./"+adr+".test")
-# sA, sC = 0, 0
-# for w in Test.sample_object.sample:
-# sA = sA + abs(A.val(w))
-# sC = sC + abs(Cible.val(w))
-# s = 0
-# for w in Test.sample_object.sample:
-# s = s + Cible.val(w)/sC*math.log(abs(A.val(w))/sA)
-# p = math.exp(-s)
-# return p
-
-# if __name__ == '__main__':
-# from sksplearn.datasets.get_dataset_path import get_dataset_path
-# adr = get_dataset_path("essai")
-# P = Learning(adr=adr, type='SPiCe')
-#
-# print("nbL = " + str(P.nbL))
-# print("nbEx = " + str(P.nbEx))
-# print("samples = " + str(P.sample))
-# print("prefixes = " + str(P.pref))
-# print("suffixes = " + str(P.suff))
-# print("factors = " + str(P.fact))
+ return - self.loss(X, y, normalize=True)
\ No newline at end of file
diff --git a/examples/graphivz-output/3.pautomac_light_man.train.gv b/splearn/tests/datasets/3.pautomac_light.train.gv
similarity index 89%
rename from examples/graphivz-output/3.pautomac_light_man.train.gv
rename to splearn/tests/datasets/3.pautomac_light.train.gv
index d0bd11b..630afa0 100644
--- a/examples/graphivz-output/3.pautomac_light_man.train.gv
+++ b/splearn/tests/datasets/3.pautomac_light.train.gv
@@ -1,24 +1,17 @@
-//automata.dot
+//3.pautomac_light.train.dot
digraph {
0 [label="0
-______
-> -0.00
-0.08 >"]
+______"]
1 [label="1
-______
-> 0.00
--0.02 >"]
+______"]
2 [label="2
______
-> -0.04
-0.45 >"]
3 [label="3
______
-> -0.11
0.63 >"]
4 [label="4
______
-> -0.09
-0.55 >"]
0 -> 1 [label="0:-0.24"]
0 -> 2 [label="0:0.35"]
diff --git a/splearn/tests/test_automaton.py b/splearn/tests/test_automaton.py
index 69dd1e8..72416fa 100644
--- a/splearn/tests/test_automaton.py
+++ b/splearn/tests/test_automaton.py
@@ -6,7 +6,8 @@ import math
from splearn.automaton import Automaton
from splearn.tests.datasets.get_dataset_path import get_dataset_path
-
+from splearn.datasets.base import load_data_sample
+from splearn import Spectral
class UnitaryTest(unittest.TestCase):
@@ -259,9 +260,9 @@ class UnitaryTest(unittest.TestCase):
A = Automaton(nbl, nbe, i, t, trans)
np.testing.assert_almost_equal(A.sum(), 1)
- def test_load_Spice_Automaton(self):
+ def test_load_Pautomac_Automaton(self):
adr = get_dataset_path("pautomac3.txt")
- A = Automaton.load_Spice_Automaton(adr)
+ A = Automaton.load_Pautomac_Automaton(adr)
r = A.nbL, A.nbS
self.assertEqual(r, (4, 25)) # à compléter
@@ -289,6 +290,20 @@ class UnitaryTest(unittest.TestCase):
dic = A.calc_prefix_completion_weights([-1])
self.assertEqual(set(dic.keys()), {0, 1, -1},
"test_calc_prefix_completion_weights failed")
+
+ def test_get_dot(self):
+ train_file = '3.pautomac_light.train'
+ data = load_data_sample(adr=get_dataset_path(train_file))
+ sp = Spectral()
+ sp.fit(X=data.data)
+ dotfile = "3.pautomac_light.train.dot"
+ dot = sp.automaton.get_dot(threshold = 0.2, title = dotfile)
+ print(dot)
+ gold_file = train_file + ".gv"
+ gold_str = ""
+ with open(get_dataset_path(gold_file), 'r') as f:
+ gold_str = f.read()
+ self.assertEqual(dot, gold_str, "test_get_dot failed")
if __name__ == '__main__':
unittest.main()
diff --git a/splearn/tests/test_hankel.py b/splearn/tests/test_hankel.py
index 8e6e34e..1057439 100644
--- a/splearn/tests/test_hankel.py
+++ b/splearn/tests/test_hankel.py
@@ -5,7 +5,7 @@ import unittest
from splearn.datasets.base import load_data_sample
from splearn.automaton import Automaton
-from splearn.spectral import Spectral, Learning
+from splearn.spectral import Spectral
from splearn.hankel import Hankel
from splearn.tests.datasets.get_dataset_path import get_dataset_path
import numpy as np
diff --git a/splearn/tests/test_spectral.py b/splearn/tests/test_spectral.py
index ce71a71..20ab7ec 100644
--- a/splearn/tests/test_spectral.py
+++ b/splearn/tests/test_spectral.py
@@ -5,7 +5,7 @@ import numpy as np
import unittest
from splearn.datasets.base import load_data_sample
from splearn.automaton import Automaton
-from splearn.spectral import Learning, Spectral
+from splearn.spectral import Spectral
from splearn.tests.datasets.get_dataset_path import get_dataset_path
@@ -17,14 +17,14 @@ class UnitaryTest(unittest.TestCase):
cl = Spectral(partial=False, sparse=False, version="prefix")
X = data.data
cl.fit(X=X)
- np.testing.assert_almost_equal(cl.Automaton.initial,
+ np.testing.assert_almost_equal(cl.automaton.initial,
np.array([-1.21159819e+00, -1.78488894e-01,
1.30974719e-02, -1.66533454e-16,
-3.70074342e-17]))
cl = Spectral(partial=False, sparse=False, version="suffix")
cl.fit(X=X)
- np.testing.assert_almost_equal(cl.Automaton.initial,
+ np.testing.assert_almost_equal(cl.automaton.initial,
np.array(
[-3.16481225e-01, -1.95397796e-01,
2.89623130e-01, 1.85499764e-15,
@@ -32,7 +32,7 @@ class UnitaryTest(unittest.TestCase):
cl = Spectral(partial=False, sparse=False,
version="factor", mode_quiet=False)
cl.fit(X=X)
- np.testing.assert_almost_equal(cl.Automaton.final,
+ np.testing.assert_almost_equal(cl.automaton.final,
np.array([ -4.35303631e-01,
1.49070953e+00,
4.80783716e-01,
@@ -55,7 +55,7 @@ class UnitaryTest(unittest.TestCase):
self.assertEqual(param.get("partial"),False)
X = np.array([[1, 2, 3, 6],[0, 0, 2, 5]])
cl.fit(X=X)
- self.assertEqual(cl.Automaton, None)
+ self.assertEqual(cl.automaton, None)
def test_fit_spectral_notsparse(self):
adr = get_dataset_path("essai")
@@ -63,7 +63,7 @@ class UnitaryTest(unittest.TestCase):
data = load_data_sample(adr=adr)
cl = Spectral(partial=False, sparse=False)
cl.fit(X=data.data)
- self.assertIsInstance(cl.Automaton,Automaton,"fit function fails")
+ self.assertIsInstance(cl.automaton,Automaton,"fit function fails")
def test_fit_spectral_sparse(self):
adr = get_dataset_path("essai")
@@ -71,7 +71,7 @@ class UnitaryTest(unittest.TestCase):
data = load_data_sample(adr=adr)
cl = Spectral(partial=False, sparse=True, rank=4)
cl.fit(X=data.data)
- self.assertIsInstance(cl.Automaton,Automaton,"fit function fails")
+ self.assertIsInstance(cl.automaton,Automaton,"fit function fails")
def test_predict_spectral(self):
@@ -108,28 +108,6 @@ class UnitaryTest(unittest.TestCase):
self.assertAlmostEquals(p, -1.7917594692280561)
- def test_constructor_learning(self):
- adr = get_dataset_path("essai")
- # adr = get_dataset_path("3.pautomac.train")
- data = load_data_sample(adr=adr)
- cl = Spectral()
- cl._polulate_dictionnaries(data.data)
- # adr = get_dataset_path("essai")
- L = Learning(data=data.data)
- with self.assertRaises(TypeError):
- data.nbL = 2.0
- # nbEx assignment
- with self.assertRaises(TypeError):
- data.nbEx = 6.0
- # # sample assignment
- # self.assertRaises(TypeError, L.splearn_object.sample, "sample")
- # # pref assignment
- # self.assertRaises(TypeError, L.splearn_object.pref, "pref")
- # # suff assignment
- # self.assertRaises(TypeError, L.splearn_object.suff, "suff")
- # # fact assignment
- # self.assertRaises(TypeError, L.splearn_object.fact, "fact")
-
def test_trigram(self):
adr = get_dataset_path("3.pautomac.train")
data = load_data_sample(adr=adr)
@@ -216,9 +194,8 @@ class UnitaryTest(unittest.TestCase):
def test_BuildAutomatonFromHankel(self): # a suivre
A = Automaton.SimpleExample()
- lh = A.BuildHankels([(), (0,), (1,)], [(), (0,), (1,)])
- B = Learning.BuildAutomatonFromHankel(lh, nbL=2, rank=2, sparse=False,
- mode_quiet=False)
+ H = A.to_hankel([(), (0,), (1,)], [(), (0,), (1,)], mode_quiet=False)
+ B = H.to_automaton(rank=2, mode_quiet=False)
np.testing.assert_almost_equal(A.val([]), B.val([]))
np.testing.assert_almost_equal(A.val([0]), B.val([0]))
np.testing.assert_almost_equal(A.val([0, 0]), B.val([0, 0]))
--
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