248 lines
7.6 KiB
Python
248 lines
7.6 KiB
Python
import fire
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import numpy as np
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from utils import *
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#from nn import compile_b
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from constants import INPUTS
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from model import Model
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from draw import draw
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from train import A_params
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def predicted_map(B, change=0, path=None):
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year = MAX_YEAR - 1
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df = pd.read_pickle('data.p')
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logger.info('temperature change of %s', change)
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inputs = list(INPUTS)
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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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frame = df[inputs + ['longitude']]
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for season in SEASONS:
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frame.loc[:, 'temp_{}_{}'.format(season, year)] += change
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print(frame.head())
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frame_cp = frame.copy()
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columns = ['latitude', 'longitude', 'biome_num']
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new_data = pd.DataFrame(columns=columns)
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nframe = pd.DataFrame(columns=frame.columns, data=normalize_ndarray(frame.to_numpy()))
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for i, (chunk, chunk_original) in enumerate(zip(chunker(nframe, B.batch_size), chunker(frame_cp, B.batch_size))):
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if chunk.shape[0] < B.batch_size:
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continue
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input_data = chunk.loc[:, inputs].values
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out = B.predict_class(input_data)
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f = pd.DataFrame({
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'longitude': chunk_original.loc[:, 'longitude'],
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'latitude': chunk_original.loc[:, 'latitude'],
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'biome_num': out
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}, columns=columns)
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new_data = new_data.append(f)
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draw(new_data, path=path)
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def predicted_map_cmd(checkpoint='checkpoints/b.h5', change=0, path=None):
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B = Model('b', epochs=1)
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B.prepare_for_use()
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B.restore(checkpoint)
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predicted_map(B, change=change, path=path)
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def predicted_temps(A, year=2000):
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columns = INPUTS
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df = pd.read_pickle('data.p')
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inputs = df[INPUTS]
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all_temps = ['temp_{}_{}'.format(season, year) for season in SEASONS]
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inputs.loc[:, 'mean_temp'] = np.mean(df[all_temps].values)
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inputs = inputs.to_numpy()
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inputs = normalize_ndarray(inputs)
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print(inputs[0:A.batch_size])
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out_columns = all_temps # + all_precips
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print(out_columns)
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out = A.predict(inputs)
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actual_output = df[out_columns][0:A.batch_size]
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model_output = pd.DataFrame(data=denormalize(out, df[out_columns].to_numpy()), columns=out_columns)[0:A.batch_size]
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print(actual_output)
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print(model_output)
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def predicted_precips(A, year=2000):
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columns = INPUTS
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df = pd.read_pickle('data.p')
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inputs = df[INPUTS]
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all_precips = ['precip_{}_{}'.format(season, year) for season in SEASONS]
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inputs.loc[:, 'mean_precip'] = np.mean(df[all_precips].values)
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inputs = inputs.to_numpy()
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inputs = normalize_ndarray(inputs)
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print(inputs[0:A.batch_size])
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out_columns = all_precips
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print(out_columns)
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out = A.predict(inputs)
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actual_output = df[out_columns][0:A.batch_size]
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model_output = pd.DataFrame(data=denormalize(out, df[out_columns].to_numpy()), columns=out_columns)[0:A.batch_size]
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print(actual_output)
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print(model_output)
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def predicted_temps_cmd(checkpoint='checkpoints/temp.h5', year=2000):
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batch_size = A_params['batch_size']['grid_search'][0]
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layers = A_params['layers']['grid_search'][0]
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optimizer = A_params['optimizer']['grid_search'][0](A_params['lr']['grid_search'][0])
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Temp = Model('temp', epochs=1)
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Temp.prepare_for_use(
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batch_size=batch_size,
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layers=layers,
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dataset_fn=dataframe_to_dataset_temp,
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optimizer=optimizer,
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out_activation=None,
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loss='mse',
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metrics=['mae']
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)
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Temp.restore(checkpoint)
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predicted_temps(Temp, year=year)
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def predicted_precips_cmd(checkpoint='checkpoints/precip.h5', year=2000):
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batch_size = A_params['batch_size']['grid_search'][0]
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layers = A_params['layers']['grid_search'][0]
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optimizer = A_params['optimizer']['grid_search'][0](A_params['lr']['grid_search'][0])
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Precip = Model('precip', epochs=1)
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Precip.prepare_for_use(
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batch_size=batch_size,
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layers=layers,
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dataset_fn=dataframe_to_dataset_temp,
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optimizer=optimizer,
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out_activation=None,
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loss='mse',
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metrics=['mae']
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)
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Precip.restore(checkpoint)
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predicted_precips(Precip, year=year)
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def predict_end_to_end(Temp, Precip, Biomes, year=2000):
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columns = INPUTS
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df = pd.read_pickle('data.p')
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inputs = df[INPUTS]
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all_temps = ['temp_{}_{}'.format(season, year) for season in SEASONS]
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inputs.loc[:, 'mean_temp'] = np.mean(df[all_temps].values)
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inputs = inputs.to_numpy()
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inputs = normalize_ndarray(inputs)
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out_columns = all_temps
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out = Temp.predict(inputs)
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temp_output = pd.DataFrame(data=denormalize(out, df[out_columns].to_numpy()), columns=out_columns)
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inputs = df[INPUTS]
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all_precips = ['precip_{}_{}'.format(season, year) for season in SEASONS]
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inputs.loc[:, 'mean_precip'] = np.mean(df[all_precips].values)
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inputs = inputs.to_numpy()
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inputs = normalize_ndarray(inputs)
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out_columns = all_precips
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out = Precip.predict(inputs)
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precip_output = pd.DataFrame(data=denormalize(out, df[out_columns].to_numpy()), columns=out_columns)
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inputs = list(INPUTS)
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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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frame = df[inputs + ['longitude']]
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for season in SEASONS:
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tc = 'temp_{}_{}'.format(season, year)
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pc = 'precip_{}_{}'.format(season, year)
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frame.loc[:, tc] = temp_output[tc]
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frame.loc[:, pc] = precip_output[pc]
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frame.loc[:, 'latitude'] = df['latitude']
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frame_cp = frame.copy()
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columns = ['latitude', 'longitude', 'biome_num']
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new_data = pd.DataFrame(columns=columns)
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nframe = pd.DataFrame(columns=frame.columns, data=normalize_ndarray(frame.to_numpy()))
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for i, (chunk, chunk_original) in enumerate(zip(chunker(nframe, Biomes.batch_size), chunker(frame_cp, Biomes.batch_size))):
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if chunk.shape[0] < Biomes.batch_size:
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continue
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input_data = chunk.loc[:, inputs].values
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out = Biomes.predict_class(input_data)
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f = pd.DataFrame({
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'longitude': chunk_original.loc[:, 'longitude'],
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'latitude': chunk_original.loc[:, 'latitude'],
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'biome_num': out
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}, columns=columns)
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new_data = new_data.append(f)
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print(new_data)
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draw(new_data)
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def predict_end_to_end_cmd(checkpoint_temp='checkpoints/temp.h5', checkpoint_precip='checkpoints/precip.h5', checkpoint_biomes='checkpoints/b.h5', year=2000):
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batch_size = A_params['batch_size']['grid_search'][0]
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layers = A_params['layers']['grid_search'][0]
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optimizer = A_params['optimizer']['grid_search'][0](A_params['lr']['grid_search'][0])
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Temp = Model('temp', epochs=1)
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Temp.prepare_for_use(
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batch_size=batch_size,
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layers=layers,
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dataset_fn=dataframe_to_dataset_temp,
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optimizer=optimizer,
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out_activation=None,
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loss='mse',
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metrics=['mae']
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)
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Temp.restore(checkpoint_temp)
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Precip = Model('precip', epochs=1)
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Precip.prepare_for_use(
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batch_size=batch_size,
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layers=layers,
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dataset_fn=dataframe_to_dataset_temp,
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optimizer=optimizer,
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out_activation=None,
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loss='mse',
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metrics=['mae']
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)
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Precip.restore(checkpoint_precip)
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Biomes = Model('b', epochs=1)
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Biomes.prepare_for_use()
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Biomes.restore(checkpoint_biomes)
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predict_end_to_end(Temp=Temp, Precip=Precip, Biomes=Biomes, year=year)
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if __name__ == "__main__":
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fire.Fire({ 'map': predicted_map_cmd, 'temp': predicted_temps_cmd, 'precip': predicted_precips_cmd, 'end-to-end': predict_end_to_end_cmd })
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