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feat(web): auto-generated form

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This commit is contained in:
Mahdi Dibaiee 2019-04-22 12:27:20 +04:30
parent e18fc7692b
commit 3bec4d7486
9 changed files with 227 additions and 171 deletions
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12
biomes/model.py
View File

@ -99,8 +99,8 @@ class Model():
def save(self, path):
logger.debug('Saving model weights to path: %s', path)
self.model.save_weights(path)
return path
self.model.save_weights(path + '/checkpoint.hd5')
return path + '/checkpoint'
def evaluate(self):
return self.model.evaluate(
@ -119,15 +119,19 @@ class Model():
# map_callback = MapHistory()
extra_params = {}
if self.class_weight:
extra_params['class_weight'] = self.class_weight
out = self.model.fit(
self.training_batched,
batch_size=self.batch_size,
epochs=self.epochs,
steps_per_epoch=int(self.TRAIN_SIZE / self.batch_size),
class_weight=self.class_weight,
validation_data=self.test_batched,
validation_steps=int(self.TEST_SIZE / self.batch_size),
verbose=1
verbose=1,
**extra_params
)
return out

59
biomes/train.py
View File

@ -4,7 +4,7 @@ import pandas as pd
import tensorflow as tf
from ray import tune
from tensorflow import keras
from utils import logger
from utils import *
from model import Model
B_params = {
@ -45,11 +45,61 @@ class TuneB(tune.Trainable):
def _restore(self, path):
return self.model.restore(path)
def start_tuning(cpu=1, gpu=2, checkpoint_freq=1, checkpoint_at_end=True, resume=False, restore=None, stop=500):
A_params = {
'batch_size': tune.grid_search([5, 16, 32, 64]),
'layers': tune.grid_search([[16, 16], [32, 32], [128, 128]]),
'lr': tune.grid_search([1e-4, 1e-3, 1e-2]),
'optimizer': tune.grid_search([tf.keras.optimizers.Adam]),
}
class TuneA(tune.Trainable):
def _setup(self, config):
logger.debug('Ray Tune model configuration %s', config)
self.model = Model('a', epochs=1)
optimizer = config['optimizer']
optimizer = config['optimizer'](lr=config['lr'])
self.model.prepare_for_use(
df=df,
batch_size=config['batch_size'],
layers=config['layers'],
optimizer=optimizer,
dataset_fn=dataframe_to_dataset_temp_precip,
loss='mse'
)
def _train(self):
logs = self.model.train(self.config)
metrics = {
'mean_accuracy': logs.history['acc'][0],
'loss': logs.history['loss'][0],
'val_accuracy': logs.history['val_acc'][0],
'val_loss': logs.history['val_loss'][0],
}
return metrics
def _save(self, checkpoint_dir):
return self.model.save(checkpoint_dir)
def _restore(self, path):
return self.model.restore(path)
def start_tuning(model, cpu=1, gpu=2, checkpoint_freq=1, checkpoint_at_end=True, resume=False, restore=None, stop=500):
ray.init()
tune.run(TuneB,
config=B_params,
if model == 'a':
t = TuneA
params = A_params
else:
t = TuneB
params = B_params
tune.run(t,
config=params,
resources_per_trial={
"cpu": cpu,
"gpu": gpu
@ -62,6 +112,5 @@ def start_tuning(cpu=1, gpu=2, checkpoint_freq=1, checkpoint_at_end=True, resume
'training_iteration': stop
})
if __name__ == "__main__":
fire.Fire(start_tuning)

90
biomes/train_temp.py
View File

@ -1,90 +0,0 @@
import fire
import ray
import pandas as pd
import tensorflow as tf
import numpy as np
from tensorflow import keras
from utils import *
from model import Model
from constants import *
CHECKPOINT = 'checkpoints/temp.h5'
SEED = 1
np.random.seed(SEED)
df = pd.read_pickle('data.p')
dataset_size, x_columns, y_columns, dataset = dataframe_to_dataset_temp_precip(df)
batch_size = 5
epochs = 500
def baseline_model():
model = keras.models.Sequential()
params = {
'kernel_initializer': 'lecun_uniform',
'bias_initializer': 'zeros',
}
model.add(keras.layers.Dense(x_columns, input_dim=x_columns, **params, activation='elu'))
model.add(keras.layers.Dense(6, **params, activation='relu'))
model.add(keras.layers.Dense(y_columns, **params))
model.compile(loss='mse', optimizer='adam', metrics=['mae'])
return model
model = baseline_model()
model.summary()
dataset = dataset.shuffle(500)
TRAIN_SIZE = int(dataset_size * 0.85)
TEST_SIZE = dataset_size - TRAIN_SIZE
(training, test) = (dataset.take(TRAIN_SIZE),
dataset.skip(TRAIN_SIZE))
training_batched = training.batch(batch_size).repeat()
test_batched = test.batch(batch_size).repeat()
logger.debug('Model dataset info: size=%s, train=%s, test=%s', dataset_size, TRAIN_SIZE, TEST_SIZE)
# model.load_weights(CHECKPOINT)
def predict():
columns = INPUTS
YEAR = 2000
print(columns)
print(df[0:batch_size])
inputs = df[columns].to_numpy()
inputs = normalize_ndarray(inputs, df[columns].to_numpy())
print(inputs[0:batch_size])
out_columns = []
for season in SEASONS:
out_columns += ['temp_{}_{}'.format(season, YEAR), 'precip_{}_{}'.format(season, YEAR)]
print(out_columns)
out = model.predict(inputs)
print(out)
print(df[out_columns][0:batch_size])
print(denormalize(out, df[out_columns].to_numpy()))
def train():
tfb_callback = tf.keras.callbacks.TensorBoard(batch_size=batch_size, log_dir='temp_logs')
checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath=CHECKPOINT, monitor='val_loss')
model.fit(training_batched,
batch_size=batch_size,
epochs=epochs,
steps_per_epoch=int(TRAIN_SIZE / batch_size),
validation_data=test_batched,
validation_steps=int(TEST_SIZE / batch_size),
callbacks=[tfb_callback, checkpoint_callback],
verbose=1)
model.save_weights(CHECKPOINT)
# train()
if __name__ == "__main__":
fire.Fire({ 'predict': predict, 'train': train })

2
biomes/utils.py
View File

@ -105,7 +105,7 @@ def dataframe_to_dataset_temp_precip(df):
tf_output = tf.cast(tf_output, tf.float32)
logger.debug('dataset size: rows=%d, input_columns=%d, num_classes=%d', int(tf_inputs.shape[0]), input_columns, num_classes)
return int(tf_inputs.shape[0]), input_columns, num_classes, tf.data.Dataset.from_tensor_slices((tf_inputs, tf_output))
return int(tf_inputs.shape[0]), input_columns, num_classes, None, tf.data.Dataset.from_tensor_slices((tf_inputs, tf_output))
flatten = lambda l: [item for sublist in l for item in sublist]

154
map-generator/index.py
View File

@ -7,32 +7,112 @@ import math
from io import BytesIO
import base64
WIDTH = 900
HEIGHT = 450
RATIO = WIDTH / HEIGHT
parameters = {
'width': {
'default': 900,
'type': 'int',
},
'height': {
'default': 450,
'type': 'int',
},
'mountain_ratio': {
'default': 0.3,
'type': 'float',
'min': 0,
'max': 1,
'step': 0.01
},
'sharpness': {
'default': 0.7,
'type': 'float',
'min': 0,
'max': 1,
'step': 0.01
},
'max_elevation': {
'default': 30,
'type': 'int',
'min': 0,
'max': 50,
},
'ground_noise': {
'default': 15,
'type': 'int',
'min': 0,
'max': 50,
},
'water_proportion': {
'default': 0.6,
'type': 'float',
'min': 0,
'max': 0.99,
'step': 0.01
},
'mountain_jaggedness': {
'default': 1,
'type': 'int',
'min': 0,
'max': 5,
},
'mountain_sea_distance': {
'default': 50,
'type': 'int',
'min': 0,
'max': 200,
},
'mountain_sea_threshold': {
'default': 2,
'type': 'int',
'min': 0,
'max': 5,
},
'water_level': {
'default': 0,
'type': 'int',
},
'mountain_area_elevation': {
'default': 0.4,
'type': 'float',
'min': 0,
'max': 1,
'step': 0.01
},
'mountain_area_elevation_n': {
'default': 5,
'type': 'int',
'min': 0,
'max': 15,
},
'mountain_area_elevation_area': {
'default': 10,
'type': 'int',
'min': 0,
'max': 25,
},
'continents': {
'default': 5,
'type': 'int',
},
'seed': {
'default': '',
'type': 'int',
'description': 'Leave empty for a random seed generated from the current timestamp.'
},
MOUNTAIN_SEA_DISTANCE = 50
MOUNTAIN_SEA_THRESHOLD = 2
MOUNTAIN_JAGGEDNESS = 1
}
p = { k: parameters[k]['default'] for k in parameters }
CONTINENT_MAX_TRIALS = 1e4
MOUNTAIN_RATIO = 0.3
SEA_COLOR = np.array((53, 179, 220, 255)) / 255
SHARPNESS = 0.7
WATER_LEVEL = 0
MAX_ELEVATION = 30
GROUND_NOISE = 15
WATER_PROPORTION = 0.6
GROUND_PROPORTION = 1 - WATER_PROPORTION
DIRECTIONS = [(-1, -1), (-1, 0), (-1, 1), (1, 1), (1, 0), (1, -1), (0, -1), (0, 1)]
def s(x):
return -2 * x**3 + 3 * x**2
def is_ground(value):
return value > WATER_LEVEL
return value > p['water_level']
def in_range(p, m, size):
x, y = p
@ -83,7 +163,7 @@ def continent_agent(ground, position, size):
if not is_ground(ground[x, y]) and in_range((x, y), position, size):
trials = 0
size -= 1
ground[x, y] = np.random.randint(1, GROUND_NOISE)
ground[x, y] = np.random.randint(1, p['ground_noise'])
else:
trials += 1
@ -91,22 +171,19 @@ def neighbours(ground, position, radius):
x, y = position
return ground[x-radius:x+radius+1, y-radius:y+radius+1]
def away_from_sea(ground, position, radius=MOUNTAIN_SEA_DISTANCE):
def away_from_sea(ground, position, radius=p['mountain_sea_distance']):
ns = neighbours(ground, position, radius).flatten()
sea = len([1 for x in ns if not is_ground(x)])
return sea < MOUNTAIN_SEA_THRESHOLD
return sea < p['mountain_sea_threshold']
MOUNTAIN_AREA_ELEVATION = 0.4
MOUNTAIN_AREA_ELEVATION_N = 5
MOUNTAIN_AREA_ELEVATION_AREA = 10
def random_elevate_agent(ground, position, height, size=MOUNTAIN_AREA_ELEVATION_N):
position = position + np.random.random_integers(-MOUNTAIN_AREA_ELEVATION_AREA, MOUNTAIN_AREA_ELEVATION_AREA, size=2)
def random_elevate_agent(ground, position, height, size=p['mountain_area_elevation_n']):
position = position + np.random.random_integers(-p['mountain_area_elevation_area'], p['mountain_area_elevation_area'], size=2)
for i in range(size):
d = DIRECTIONS[np.random.randint(len(DIRECTIONS))]
change = height * MOUNTAIN_AREA_ELEVATION + np.random.randint(MOUNTAIN_JAGGEDNESS + 1)
change = height * p['mountain_area_elevation'] + np.random.randint(p['mountain_jaggedness'] + 1)
new_index = bound_check(ground, position + np.array(d))
if is_ground(ground[new_index]):
@ -118,21 +195,21 @@ def mountain_agent(ground, position):
return
x, y = position
height = np.random.randint(MAX_ELEVATION)
height = np.random.randint(p['max_elevation'])
ground[x, y] = height
last_height = height
for i in range(1, height):
for d in DIRECTIONS:
change = np.random.randint(MOUNTAIN_JAGGEDNESS + 1)
change = np.random.randint(p['mountain_jaggedness'] + 1)
distance = np.array(d)*i
new_index = bound_check(ground, position + distance)
if is_ground(ground[new_index]):
ground[new_index] = last_height - change
last_height = last_height - MOUNTAIN_JAGGEDNESS
last_height = last_height - p['mountain_jaggedness']
random_elevate_agent(ground, position, height)
@ -141,13 +218,22 @@ def mountain_agent(ground, position):
# def split_agent(ground, position, directions):
def constant_filter(a):
if a[0] > (1 - SHARPNESS):
if a[0] > (1 - p['sharpness']):
return max(1, a[0])
return 0
def generate_map(width=WIDTH, height=HEIGHT, continents=4):
def generate_map(**kwargs):
plt.clf()
p.update(kwargs)
np.random.seed(p['seed'] or None)
width, height = p['width'], p['height']
continents = p['continents']
ground = np.zeros((width, height))
ground_size = width * height * GROUND_PROPORTION
ground_size = width * height * (1 - p['water_proportion'])
# position = (int(width / 2), int(height / 2))
# ground_size = width * height * GROUND_PROPORTION
@ -157,9 +243,9 @@ def generate_map(width=WIDTH, height=HEIGHT, continents=4):
print(position)
continent_agent(ground, position, size=ground_size)
ground = ndimage.gaussian_filter(ground, sigma=(1 - SHARPNESS) * 20)
ground = ndimage.gaussian_filter(ground, sigma=(1 - p['sharpness']) * 20)
for i in range(int(ground_size * MOUNTAIN_RATIO / MAX_ELEVATION**2)):
for i in range(int(ground_size * p['mountain_ratio'] / p['max_elevation']**2)):
position = (np.random.randint(0, width), np.random.randint(0, height))
mountain_agent(ground, position)
@ -169,7 +255,7 @@ def generate_map(width=WIDTH, height=HEIGHT, continents=4):
ground = ndimage.gaussian_filter(ground, sigma=4)
ground = ndimage.generic_filter(ground, constant_filter, size=1)
print(np.min(ground), np.max(ground), MAX_ELEVATION)
print(np.min(ground), np.max(ground), p['max_elevation'])
print(np.unique(ground))
plt.imshow(ground.T, cmap=greys, norm=norm)
@ -181,5 +267,5 @@ def generate_map(width=WIDTH, height=HEIGHT, continents=4):
return figfile
if __name__ == "__main__":
generate_map(WIDTH, HEIGHT)
generate_map()
plt.show()

19
map-generator/static/script.js
View File

@ -3,15 +3,26 @@ const mapSettings = document.getElementById('map-settings');
const map = document.getElementById('map');
const spinner = document.getElementById('spinner');
function generate() {
spinner.classList.remove('d-none');
const formData = new FormData(mapSettings)
if (!formData.get('seed')) {
formData.set('seed', (new Date()).getTime() % 1e5);
}
const queryString = new URLSearchParams(formData).toString()
map.src = '/map?' + queryString;
map.classList.add('d-none');
}
mapSettings.addEventListener('submit', (e) => {
console.log('form submit');
e.preventDefault();
spinner.classList.remove('d-none');
map.src = '/map?q=' + (new Date()).getTime();
map.classList.add('d-none');
generate()
});
generate()
map.addEventListener('load', () => {
spinner.classList.add('d-none');
map.classList.remove('d-none');

1
map-generator/static/style.css
View File

@ -10,5 +10,6 @@ aside {
text-align: center;
width: 300px;
height: 100vh;
overflow-y: scroll;
}

41
map-generator/templates/index.html
View File

@ -11,7 +11,7 @@
<div class='row'>
<main class='col d-flex justify-content-center align-items-center'>
<!-- <canvas id='board'></canvas> -->
<img src='/map' id='map'>
<img src='' id='map'>
<div class='spinner-border text-primary' role='status' id='spinner'>
<span class='sr-only'>Loading...</span>
@ -23,33 +23,18 @@
<div class='panel px-4'>
<form class='mt-5' id='map-settings'>
<div class='form-group'>
<label name='preset'>Preset</label>
<select id='preset' name='preset' class='form-control'>
<option value='desert'>Desert</option>
<option value='forest'>Forest</option>
</select>
</div>
<div class='form-group'>
<label name='mean-temperature'>Mean Temperature</label>
<input id='mean-temperature' name='mean-temperature' type='range' min='-50' max='50' class='form-control'>
</div>
<div class='form-group'>
<label name='mean-precipitation'>Mean Precipitation</label>
<input id='mean-precipitation' name='mean-precipitation' type='range' min='-50' max='50' class='form-control'>
</div>
<div class='form-group'>
<label name='min-elevation'>Minimum Elevation</label>
<input id='min-elevation' name='min-elevation' type='range' min='-100' max='100' class='form-control'>
</div>
<div class='form-group'>
<label name='max-elevation'>Maximum Elevation</label>
<input id='max-elevation' name='max-elevation' type='range' min='-100' max='100' class='form-control'>
</div>
{% for k, v in parameters.items() %}
<div class='form-group'>
<label name='{{ k }}'>{{ k | replace('_', ' ') | title }}</label>
<input id='{{ k }}' name='{{ k }}'
type="number"
class='form-control'
min='{{ v["min"] }}' value='{{ v["default"] }}' max='{{ v["max"] }}' step='{{ v["step"] }}'>
{% if v["description"] %}
<small class="form-text text-muted">{{ v["description"] }}</small>
{% endif %}
</div>
{% endfor %}
<button type="submit" class="btn btn-primary">Generate</button>
</form>

18
map-generator/web.py
View File

@ -1,13 +1,23 @@
from flask import Flask, render_template, make_response, send_file
from index import generate_map
from flask import Flask, render_template, make_response, send_file, request
from index import generate_map, parameters
app = Flask(__name__)
@app.route('/')
def index():
return render_template('index.html')
return render_template('index.html', parameters=parameters)
def parse(key, value):
t = parameters[key]['type']
if t == 'int':
return int(value)
elif t == 'float':
return float(value)
else:
return value
@app.route('/map')
def get_map():
res = send_file(generate_map(), mimetype='image/png')
params = { key: parse(key, request.args[key]) for key in request.args }
res = send_file(generate_map(**params), mimetype='image/png')
return res