feat(temps): various temperatures
This commit is contained in:
Mahdi Dibaiee
parent
d8365d6285
commit
f268e72244
42
draw.py
42
draw.py
@ -4,20 +4,23 @@ import matplotlib.pyplot as plt
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import pandas as pd
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import cartopy.crs as ccrs
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df = pd.read_pickle('data_final.p')
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def draw(df):
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biomes = {}
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biome_numbers = df['biome_num'].unique()
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# biome_names = df['biome_name'].unique()
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biomes = {}
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biome_numbers = df['biome_num'].unique()
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for n in biome_numbers:
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biomes[n] = []
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for (longitude, latitude), row in df.iterrows():
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p = Point(longitude, latitude)
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if row.biome_num in biomes:
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biomes[row.biome_num].append(p)
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else:
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biomes[row.biome_num] = [p]
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for (longitude, latitude), row in df.iterrows():
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biomes[row.biome_num].append(Point(longitude, latitude))
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ax = plt.axes(projection=ccrs.PlateCarree())
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ax.stock_img()
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# ax.legend(df['biome_name'].unique())
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ax = plt.axes(projection=ccrs.PlateCarree())
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ax.stock_img()
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colors={
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colors={
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0: '#016936',
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1: '#B2D127',
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2: '#77CC00',
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@ -32,10 +35,21 @@ colors={
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11: '#00850F',
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12: '#FF9E1F',
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13: '#FF1F97'
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}
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}
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for n in biome_numbers:
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for n in biome_numbers:
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biomes[n] = MultiPoint(biomes[n]).buffer(1)
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# print(biomes[n])
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# legend = biome_names[n]
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if not hasattr(biomes[n], '__iter__'):
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biomes[n] = [biomes[n]]
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ax.add_geometries(biomes[n], ccrs.PlateCarree(), facecolor=colors[n])
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# artist.set_label(biome_names[n])
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# print(artist.get_label())
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plt.show()
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# ax.legend(artists, biome_names)
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plt.show()
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if __name__ == "__main__":
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df = pd.read_pickle('data_final.p')
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draw(df)
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112
nn.py
Normal file
112
nn.py
Normal file
@ -0,0 +1,112 @@
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from __future__ import absolute_import, division, print_function
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# TensorFlow and tf.keras
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import tensorflow as tf
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from tensorflow import keras
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# Helper libraries
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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import os.path
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from utils import *
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RANDOM_SEED = 1
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tf.enable_eager_execution()
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tf.set_random_seed(RANDOM_SEED)
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np.random.seed(RANDOM_SEED)
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df = pd.read_pickle('data_final.p')
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class Model():
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def __init__(self, name, batch_size=100, shuffle_buffer_size=500, learning_rate=0.001, epochs=1):
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self.name = name
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self.path = "checkpoints/{}.hdf5".format(name)
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self.batch_size = batch_size
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self.shuffle_buffer_size = shuffle_buffer_size
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self.learning_rate = learning_rate
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self.epochs = epochs
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def prepare_dataset(self, df, fn):
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self.dataset_fn = fn
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dataset_size, features, output_size, dataset = fn(df)
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self.dataset = dataset.shuffle(self.shuffle_buffer_size)
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self.TRAIN_SIZE = int(dataset_size * 0.85)
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self.TEST_SIZE = dataset_size - self.TRAIN_SIZE
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(training, test) = (self.dataset.take(self.TRAIN_SIZE).batch(self.batch_size).repeat(),
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self.dataset.skip(self.TRAIN_SIZE).batch(self.batch_size).repeat())
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self.dataset_size = dataset_size
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self.features = features
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self.output_size = output_size
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self.training = training
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self.test = test
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def create_model(self, layers):
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self.model = keras.Sequential([
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keras.layers.Dense(layers[0], activation=tf.nn.relu, input_shape=[self.features])
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] + [
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keras.layers.Dense(n, activation=tf.nn.relu) for n in layers[1:]
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] + [
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keras.layers.Dense(self.output_size)
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])
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def compile(self):
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self.model.load_weights(self.path)
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optimizer = tf.train.AdamOptimizer(self.learning_rate)
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self.model.compile(loss='mse',
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optimizer=optimizer,
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metrics=['mae', 'accuracy'])
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def evaluate(self):
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return self.model.evaluate(
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self.test,
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batch_size=self.batch_size,
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steps=int(self.dataset_size / self.batch_size),
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verbose=1
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)
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def train(self):
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self.model.summary()
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checkpoint = keras.callbacks.ModelCheckpoint(filepath, monitor='acc', verbose=1, mode='max')
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self.model.fit(
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self.training,
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batch_size=self.batch_size,
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epochs=self.epochs,
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steps_per_epoch=int(self.dataset_size / self.batch_size),
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callbacks=[checkpoint],
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verbose=1
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)
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def predict(self, a):
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return np.argmax(self.model.predict(a), axis=1)
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A = Model('a', batch_size=100, shuffle_buffer_size=500, learning_rate=0.001, epochs=2)
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B = Model('b', batch_size=100, shuffle_buffer_size=500, learning_rate=0.001, epochs=850)
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if __name__ == "__main__":
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B.prepare_dataset(df, dataframe_to_dataset_biomes)
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B.create_model([64, 128])
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B.compile()
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# for inp, out in B.test.take(1).make_one_shot_iterator():
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# print(inp, out)
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# print(np.unique(nums))
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# print(np.unique(predictions))
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print('loss: {}, evaluation: {}'.format(*B.evaluate()))
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# B.train()
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A.prepare_dataset(df, dataframe_to_dataset_temp_precip)
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A.create_model([4])
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A.compile()
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# A.train()
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134
train.py
134
train.py
@ -1,134 +0,0 @@
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from __future__ import absolute_import, division, print_function
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# TensorFlow and tf.keras
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import tensorflow as tf
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from tensorflow import keras
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# Helper libraries
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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import os.path
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from utils import *
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RANDOM_SEED = 1
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tf.enable_eager_execution()
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tf.set_random_seed(RANDOM_SEED)
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np.random.seed(RANDOM_SEED)
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df = pd.read_pickle('data_final.p')
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# temp and precipitation
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def train_model_a():
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filepath = "checkpoints/a.hdf5"
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BATCH_SIZE = 100
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SHUFFLE_BUFFER_SIZE = 500
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LEARNING_RATE = 0.001
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EPOCHS = 2
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# dataset = dataframe_to_dataset_biomes(df)
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dataset_size, features, output_size, dataset = dataframe_to_dataset_temp_precip(df)
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dataset = dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)
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TRAIN_SIZE = dataset_size * 0.85
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TEST_SIZE = dataset_size - TRAIN_SIZE
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(training, test) = (dataset.take(TRAIN_SIZE).repeat(), dataset.skip(TRAIN_SIZE).repeat())
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model = keras.Sequential([
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keras.layers.Dense(4, activation=tf.nn.relu, input_shape=[features]),
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keras.layers.Dense(output_size)
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])
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model.load_weights(filepath)
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optimizer = tf.train.AdamOptimizer(LEARNING_RATE)
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model.compile(loss='mse',
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optimizer=optimizer,
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metrics=['mae', 'accuracy'])
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model.summary()
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checkpoint = keras.callbacks.ModelCheckpoint(filepath, monitor='acc', verbose=1, mode='max')
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model.fit(
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training,
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batch_size=BATCH_SIZE,
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epochs=EPOCHS,
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steps_per_epoch=int(dataset_size / BATCH_SIZE),
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callbacks=[checkpoint],
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verbose=1
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)
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evaluation = model.evaluate(
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test,
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batch_size=BATCH_SIZE,
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steps=int(dataset_size / BATCH_SIZE),
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verbose=1
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)
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print(evaluation)
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# 850 epochs so far
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def train_model_b():
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filepath = filepath="checkpoints/b.hdf5"
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BATCH_SIZE = 100
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SHUFFLE_BUFFER_SIZE = 500
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LEARNING_RATE = 0.0005
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EPOCHS = 400
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# dataset = dataframe_to_dataset_biomes(df)
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dataset_size, features, output_size, dataset = dataframe_to_dataset_biomes(df)
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dataset = dataset.shuffle(SHUFFLE_BUFFER_SIZE)
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TRAIN_SIZE = dataset_size * 0.85
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TEST_SIZE = dataset_size - TRAIN_SIZE
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(training, test) = (dataset.take(TRAIN_SIZE).batch(BATCH_SIZE).repeat(), dataset.skip(TRAIN_SIZE).batch(BATCH_SIZE).repeat())
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model = keras.Sequential([
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keras.layers.Dense(64, activation=tf.nn.relu, input_shape=[features]),
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keras.layers.Dense(128, activation=tf.nn.relu),
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keras.layers.Dense(output_size, activation=tf.nn.softmax)
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])
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model.load_weights(filepath)
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optimizer = tf.train.AdamOptimizer(LEARNING_RATE)
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model.compile(loss='sparse_categorical_crossentropy',
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optimizer=optimizer,
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metrics=['accuracy'])
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model.summary()
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checkpoint = keras.callbacks.ModelCheckpoint(filepath, monitor='acc', verbose=1, mode='max')
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model.fit(
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training,
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epochs=EPOCHS,
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verbose=1,
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steps_per_epoch=int(dataset_size / BATCH_SIZE),
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callbacks=[checkpoint]
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)
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# print(dataset.repeat().make_one_shot_iteraor().get_next())
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# inp, out = test.make_one_shot_iterator().get_next()
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# print(inp, out)
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# print(np.argmax(model.predict(inp), axis=1))
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evaluation = model.evaluate(
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test,
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batch_size=BATCH_SIZE,
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steps=int(dataset_size / BATCH_SIZE),
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verbose=1
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)
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print('loss: {}, accuracy: {}'.format(*evaluation))
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# train_model_a()
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train_model_b()
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# train_model_a()
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2
utils.py
2
utils.py
@ -18,7 +18,7 @@ def normalize_ndarray(ar):
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def normalize_df(df):
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for col in df.columns:
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df[col] = normalize(df[col])
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df.loc[col] = normalize(df[col])
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return df
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65
various_temps.py
Normal file
65
various_temps.py
Normal file
@ -0,0 +1,65 @@
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import numpy as np
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from utils import *
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from nn import B
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from draw import draw
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import time
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def chunker(seq, size):
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return (seq[pos:pos + size] for pos in range(0, len(seq), size))
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year = MAX_YEAR - 1
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df = pd.read_pickle('data_final.p')
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latitude = np.array(df.index.get_level_values(1))
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df.loc[:, 'latitude'] = pd.Series(latitude, index=df.index)
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B.prepare_dataset(df, dataframe_to_dataset_biomes)
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B.create_model([64, 128])
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B.compile()
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for change in range(-5, 6):
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print('TEMPERATURE MODIFICATION OF {}'.format(change))
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inputs = ['elevation', 'distance_to_water']
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for season in SEASONS:
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inputs += [
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'temp_{}_{}'.format(season, year),
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'precip_{}_{}'.format(season, year)
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]
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inputs += ['latitude']
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frame = df[inputs]
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print(frame.head())
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# for season in SEASONS:
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# frame.loc[:, 'temp_{}_{}'.format(season, year)] += change
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# print(np.average(frame.loc[:, 'temp_winter_2016']))
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# index = []
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# for longitude in range(-179, 179):
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# for latitude in range(-89, 89):
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# index.append((longitude, latitude))
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columns = ['biome_num']
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new_data = pd.DataFrame(columns=columns)
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for i, chunk in enumerate(chunker(frame, B.batch_size)):
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input_data = normalize_ndarray(chunk.values)
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out = B.predict(input_data)
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new_index = np.concatenate((chunk.index.values, new_data.index.values))
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new_data = new_data.reindex(new_index)
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new_data.loc[chunk.index.values, 'biome_num'] = out
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# print(new_data['biome_num'].unique())
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draw(new_data)
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# columns = ['biome_num']
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# indices = ['longitude', 'latitude']
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# new_df = pd.DataFrame(index=indices, columns=columns)
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# new_df =
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