Adapt wut? for witzit.
jebba
parent
1a2f1dba74
commit
181ea45f15
20
README.md
20
README.md
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@ -41,7 +41,6 @@ pip install --user --upgrade -r requirements.txt
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Development is most easily done under Jupyter with Tensorboard
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for training models. These files are in the `notebooks/` directory.
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* `witzit.ipynb` --- witzit Jupyter notebook.
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* `witzit-predict.ipynb` --- witzit Jupyter notebook, prediction application.
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* `witzit-train.ipynb` --- witzit Jupyter notebook, training application.
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@ -55,6 +54,25 @@ a main original archive of 10,000 samples and you want to process just 1,000
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of them, they would be copied to the `data/` directory.
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Data is also stored here, which can also be deleted/moved by scripts:
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`/srv/witzit/`
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Each element sample will be stored under here:
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`/srv/witzit/data/element/`
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Each element model will be stored under here:
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`/srv/witzit/data/models/`
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Temporary logs during training may be written to the gitignored
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`logs/` directory.
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# Usage
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HOWTO USE. Getting closer...
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@ -33,24 +33,6 @@
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"# https://spacecruft.org/spacecruft/satnogs-wut"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# GPLv3+"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Built using Jupyter, Tensorflow, Keras"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@ -183,7 +165,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"model = load_model('data/models/witzit-DUV.tf')"
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"model = load_model('data/models/witzit-al.tf')"
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]
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},
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{
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@ -76,7 +76,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"ENCODING='GMSK'\n",
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"ELEMENT='al'\n",
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"batch_size = 128\n",
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"epochs = 4\n",
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"IMG_WIDTH = 416\n",
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@ -89,9 +89,9 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"train_dir = os.path.join('/srv/satnogs/data/txmodes', ENCODING )\n",
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"train_dir = os.path.join('/srv/satnogs/data/txmodes', ENCODING, 'train')\n",
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"val_dir = os.path.join('/srv/satnogs/data/txmodes', ENCODING, 'val')\n",
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"train_dir = os.path.join('/srv/witzit/data/element', ELEMENT )\n",
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"train_dir = os.path.join('/srv/witzit/data/element', ELEMENT, 'train')\n",
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"val_dir = os.path.join('/srv/witzit/data/element', ELEMENT, 'val')\n",
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"train_good_dir = os.path.join(train_dir, 'good')\n",
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"train_bad_dir = os.path.join(train_dir, 'bad')\n",
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"val_good_dir = os.path.join(val_dir, 'good')\n",
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@ -112,19 +112,19 @@
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"source": [
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"print('total training good images:', num_train_good)\n",
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"print('total training bad images:', num_train_bad)\n",
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"#print(\"--\")\n",
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"print(\"--\")\n",
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"print(\"Total training images:\", total_train)\n",
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"#print('total validation good images:', num_val_good)\n",
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"#print('total validation bad images:', num_val_bad)\n",
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"#print(\"--\")\n",
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"#print(\"Total validation images:\", total_val)\n",
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"#print(\"Reduce training and validation set when testing\")\n",
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"print('total validation good images:', num_val_good)\n",
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"print('total validation bad images:', num_val_bad)\n",
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"print(\"--\")\n",
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"print(\"Total validation images:\", total_val)\n",
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"print(\"Reduce training and validation set when testing\")\n",
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"total_train = 100\n",
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"total_val = 100\n",
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"#print(\"Train =\")\n",
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"#print(total_train)\n",
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"#print(\"Validation =\")\n",
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"#print(total_val)"
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"print(\"Train =\")\n",
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"print(total_train)\n",
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"print(\"Validation =\")\n",
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"print(total_val)"
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]
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},
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{
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@ -371,7 +371,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"model.save('/srv/satnogs/data/models/GMSK/witzit-GMSK-201912.h5')"
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"model.save('/srv/witzit/data/models/al/witzit-al.h5')"
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]
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},
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{
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@ -380,7 +380,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"model.save('/srv/satnogs/data/models/GMSK/witzit-GMSK-201912.tf')"
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"model.save('/srv/witzit/data/models/al/witzit-al.tf')"
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]
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},
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{
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@ -398,7 +398,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"plot_model(model, show_shapes=True, show_layer_names=True, expand_nested=True, dpi=72, to_file='/srv/satnogs/data/models/GMSK/plot_model.png')"
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"plot_model(model, show_shapes=True, show_layer_names=True, expand_nested=True, dpi=72, to_file='/srv/witzit/data/models/al/plot_model.png')"
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]
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},
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{
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@ -1,758 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# witzit --- What In The Zap Is That? AI categorization of spectra from LIBS/XRF analyzers\n",
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"#\n",
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"# Copyright (C) 2019-2022, Jeff Moe\n",
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"#\n",
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"# This program is free software: you can redistribute it and/or modify\n",
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"# it under the terms of the GNU General Public License as published by\n",
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"# the Free Software Foundation, either version 3 of the License, or\n",
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"# (at your option) any later version.\n",
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"#\n",
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"# This program is distributed in the hope that it will be useful,\n",
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"# but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
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"# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n",
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"# GNU General Public License for more details.\n",
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"#\n",
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"# You should have received a copy of the GNU General Public License\n",
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"# along with this program. If not, see <http://www.gnu.org/licenses/>.\n",
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"#\n",
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"# https://spacecruft.org/spacecruft/witzit\n",
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"#\n",
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"# Based on wut?\n",
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"# https://spacecruft.org/spacecruft/satnogs-wut"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"Start\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow.python.keras"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from tensorflow.python.keras import Sequential\n",
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"from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense\n",
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"from tensorflow.python.keras.preprocessing.image import ImageDataGenerator\n",
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"from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
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"from tensorflow.python.keras import optimizers\n",
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"from tensorflow.python.keras.preprocessing import image\n",
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"from tensorflow.python.keras.models import load_model\n",
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"from tensorflow.python.keras.preprocessing.image import load_img\n",
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"from tensorflow.python.keras.preprocessing.image import img_to_array"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from tensorflow.python.keras.models import Model\n",
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"from tensorflow.python.keras.layers import Input, concatenate"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Visualization\n",
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn.decomposition import PCA"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Seaborn pip dependency\n",
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"import seaborn as sns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Interact\n",
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"# https://ipywidgets.readthedocs.io/en/stable/examples/Using%20Interact.html\n",
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"from __future__ import print_function\n",
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"from ipywidgets import interact, interactive, fixed, interact_manual\n",
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"import ipywidgets as widgets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Display Images\n",
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"from IPython.display import display, Image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"Python import done\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_dir = os.path.join('data/', 'train')\n",
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"val_dir = os.path.join('data/', 'val')\n",
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"test_dir = os.path.join('data/', 'test')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_good_dir = os.path.join(train_dir, 'good')\n",
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"train_bad_dir = os.path.join(train_dir, 'bad')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"val_good_dir = os.path.join(val_dir, 'good')\n",
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"val_bad_dir = os.path.join(val_dir, 'bad')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"num_train_good = len(os.listdir(train_good_dir))\n",
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"num_train_bad = len(os.listdir(train_bad_dir))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"num_val_good = len(os.listdir(val_good_dir))\n",
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"num_val_bad = len(os.listdir(val_bad_dir))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"num_test = len(os.listdir(test_dir))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"total_train = num_train_good + num_train_bad\n",
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"total_val = num_val_good + num_val_bad"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print('total training good images:', num_train_good)\n",
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"print('total training bad images:', num_train_bad)\n",
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"print(\"--\")\n",
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"print(\"Total training images:\", total_train)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print('total validation good images:', num_val_good)\n",
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"print('total validation bad images:', num_val_bad)\n",
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"print(\"--\")\n",
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"print(\"Total validation images:\", total_val)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"Reduce training and validation set when testing\")\n",
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"#total_train = 500\n",
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"#total_val = 500\n",
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"print(\"Train =\")\n",
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"print(total_train)\n",
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"print(\"Validation =\")\n",
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"print(total_val)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Good results\n",
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"#batch_size = 128\n",
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"#epochs = 6\n",
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"#\n",
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"# Testing, faster more inaccurate results\n",
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"batch_size = 96\n",
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"epochs = 3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Half size\n",
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"IMG_HEIGHT = 416\n",
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"IMG_WIDTH= 804\n",
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"# Full size, machine barfs probably needs more RAM\n",
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"#IMG_HEIGHT = 832\n",
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"#IMG_WIDTH = 1606"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_image_generator = ImageDataGenerator(\n",
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" rescale=1./255\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"val_image_generator = ImageDataGenerator(\n",
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" rescale=1./255\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"test_image_generator = ImageDataGenerator(\n",
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" rescale=1./255\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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||||
"source": [
|
||||
"train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,\n",
|
||||
" directory=train_dir,\n",
|
||||
" shuffle=True,\n",
|
||||
" target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
|
||||
" class_mode='binary')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"val_data_gen = val_image_generator.flow_from_directory(batch_size=batch_size,\n",
|
||||
" directory=val_dir,\n",
|
||||
" target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
|
||||
" class_mode='binary')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(test_dir)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_data_gen = test_image_generator.flow_from_directory(batch_size=batch_size,\n",
|
||||
" directory=test_dir,\n",
|
||||
" target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
|
||||
" class_mode='binary')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sample_train_images, _ = next(train_data_gen)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sample_val_images, _ = next(val_data_gen)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sample_test_images, _ = next(test_data_gen)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# This function will plot images in the form of a grid with 1 row and 3 columns where images are placed in each column.\n",
|
||||
"def plotImages(images_arr):\n",
|
||||
" fig, axes = plt.subplots(1, 3, figsize=(20,20))\n",
|
||||
" axes = axes.flatten()\n",
|
||||
" for img, ax in zip( images_arr, axes):\n",
|
||||
" ax.imshow(img)\n",
|
||||
" ax.axis('off')\n",
|
||||
" plt.tight_layout()\n",
|
||||
" plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plotImages(sample_train_images[0:3])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plotImages(sample_val_images[0:3])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plotImages(sample_test_images[0:3])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = Sequential([\n",
|
||||
" Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH ,3)),\n",
|
||||
" MaxPooling2D(),\n",
|
||||
" Conv2D(32, 3, padding='same', activation='relu'),\n",
|
||||
" MaxPooling2D(),\n",
|
||||
" Conv2D(64, 3, padding='same', activation='relu'),\n",
|
||||
" MaxPooling2D(),\n",
|
||||
" Flatten(),\n",
|
||||
" Dense(512, activation='relu'),\n",
|
||||
" Dense(1, activation='sigmoid')\n",
|
||||
"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.compile(optimizer='adam',\n",
|
||||
" loss='binary_crossentropy',\n",
|
||||
" metrics=['accuracy'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.summary()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Image.LOAD_TRUNCATED_IMAGES = True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"history = model.fit_generator(\n",
|
||||
" train_data_gen,\n",
|
||||
" steps_per_epoch=total_train // batch_size,\n",
|
||||
" epochs=epochs,\n",
|
||||
" validation_data=val_data_gen,\n",
|
||||
" validation_steps=total_val // batch_size,\n",
|
||||
" verbose=1\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"acc = history.history['accuracy']\n",
|
||||
"val_acc = history.history['val_accuracy']\n",
|
||||
"\n",
|
||||
"loss = history.history['loss']\n",
|
||||
"val_loss = history.history['val_loss']\n",
|
||||
"\n",
|
||||
"epochs_range = range(epochs)\n",
|
||||
"\n",
|
||||
"plt.figure(figsize=(8, 8))\n",
|
||||
"plt.subplot(1, 2, 1)\n",
|
||||
"plt.plot(epochs_range, acc, label='Training Accuracy')\n",
|
||||
"plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
|
||||
"plt.legend(loc='lower right')\n",
|
||||
"plt.title('Training and Validation Accuracy')\n",
|
||||
"\n",
|
||||
"plt.subplot(1, 2, 2)\n",
|
||||
"plt.plot(epochs_range, loss, label='Training Loss')\n",
|
||||
"plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
|
||||
"plt.legend(loc='upper right')\n",
|
||||
"plt.title('Training and Validation Loss')\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"TRAINING info\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(train_dir)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(train_good_dir)\n",
|
||||
"print(train_bad_dir)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(train_image_generator)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(train_data_gen)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#print(sample_train_images)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(history)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.save('data/witzit-CW.h5')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# https://keras.io/models/sequential/\n",
|
||||
"print(\"predict\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pred=model.predict_generator(test_data_gen,\n",
|
||||
"steps=1,\n",
|
||||
"verbose=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prediction = model.predict(\n",
|
||||
" x=test_data_gen,\n",
|
||||
" verbose=2\n",
|
||||
")\n",
|
||||
"print(\"end predict\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"predictions=[]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Show prediction score\n",
|
||||
"print(prediction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prediction_bool = (prediction >0.8)\n",
|
||||
"print(prediction_bool)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"predictions = prediction_bool.astype(int)\n",
|
||||
"print(predictions)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Make final prediction\n",
|
||||
"# XXX, display name, display all of them with mini waterfall, etc.\n",
|
||||
"if prediction_bool[0] == False:\n",
|
||||
" rating = 'bad'\n",
|
||||
"else:\n",
|
||||
" rating = 'good'\n",
|
||||
"print('Observation: %s' % (rating))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if prediction_bool[1] == False:\n",
|
||||
" rating = 'bad'\n",
|
||||
"else:\n",
|
||||
" rating = 'good'\n",
|
||||
"print('Observation: %s' % (rating))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if prediction_bool[2] == False:\n",
|
||||
" rating = 'bad'\n",
|
||||
"else:\n",
|
||||
" rating = 'good'\n",
|
||||
"print('Observation: %s' % (rating))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# The End"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Loading…
Reference in New Issue